diff --git "a/dataset/train_dataset.csv" "b/dataset/train_dataset.csv"
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-Unnamed: 0,Question,Sample ANS,Student ANS,Score
-1,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to verify/test the efficiency of an algorithm as there are multiple ways/algorithms to solve the same problem/issue.,2.5
-2,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to determine the complexity of an algorithm. some algorithm takes complexity of O(n) while some are of the order of O(logn) and some are of exponential time complexity so to determine time complexity and analysis time we need to analyze algorithm,2.5
-3,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis so we can analysis time complexity of different  algorithms and compare them with each other and use the algorithm whos complexity is less than others,2.5
-4,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis for finding the best function to perform a particular task. This is done by finding an algorithm that takes the least time complexity as well as least auxiliary space(space Complexity). Though many algorithm work equally efficiently for smaller datasets, but the best algorithm is found when it runs the most efficiently for very large amount of data.  ",2.5
-5,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We require algorithm analysis so that we can analyze the efficiency of any particular algorithm. There are many ways to solve any given problem, but we want to consider the best possible algorithm that is time efficient and memory efficient.",2.5
-6,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is used to find the time complexity and space complexity of a particular algorithm. \nWe analyze all the cases of algorithm like best, worst  and average cases.",2.5
-7,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",,0.0
-8,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We do algorithm analysis to check the time and space complexity of the algorithm and to reduce  the time and space complexity of the algorithm so that the algorithm can be implemented in less time and more efficiently.,2.5
-9,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis because there are many complex operations which when done with normal methods will take forever to complete and very large space. Algorithm analysis is needed to save time and space for such complex operations. ,2.5
-10,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to understand the program efficiently and compute the code by solving the program step by step,2.5
-11,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to study the problem and to give a best or perfect solution of it by using least time complexity and space complexity so that the problem can be solved efficiently and correctly .,2.5
-12,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is needed to get the better understanding of the algorithms or to get the use and optimized solution for any problem that can reduce the time and space complexity.,2.5
-13,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We use algorithm analysis for analyzing different problems and programs to run . And to identify new various method and solutions.,2.5
-14,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis in order to judge the  space and time complexity of an algorithm to function. The lesser the amount of time and space taken the better and more efficient the algorithm is in solving complex problems.,2.5
-15,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is done to know the time and space complexity for a program so that a task can be done most efficiently.,2.5
-16,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To analise the efficiency of algorithm.\n- To compute Space and Time complexity.\n- Check whether algorithm solve actual problem or not.,2.5
-17,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis helps us approximately calculate the time and space complexity of our algorithm.\nSince running algorithms at a large scale includes high costs...\nso algorithm analysis helps us to optimize the algorithm to lower the actual running costs.,2.5
-18,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis because on industrial or big scale we always require a code whose time and space complexity are minimum. Hence, algorithm analysis helps us in calculating the space and time complexity.",2.5
-19,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","1. In order to analysis the time complexity the efficient time algorithm will take to complete  ,space complexity  (created due to creation of arrays ,variables) of the algorithm.\n2. To detect which is the best suited algorithm which covers all the worst cases, edges cases of the algorithms.\n3.is also helps to eliminate Time limit Exceeded of the algorithm.\n4. symptotic and asymptotic analysis can be made. ",2.5
-20,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to understand the code deeply and to know how the code actually functions.\nAlso algorithm analysis helps us to find the time complexity of the algorithm and thus without actually having to implement the code on the compiler, we can get the approximate time in which the algorithm will produce the output.\nAlgorithm analysis also helps us to find the space complexity and helps us to realize which algorithm best suits the question.",2.5
-21,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to improve the efficiency of the program by reducing time and space complexities and also to find out ant problems and deficiencies in the program. Algorithm analysis helps us in overcoming all the underlying factors and understand the program better.,2.5
-22,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm Analysis is related to improving the efficiency of a program by reducing the time complexities as well as the space complexities . Also algorithm analysis is used to find out any problems or deficiencies in the program.,2.5
-23,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm Analysis is needed to know the complexities ,the optimisation ,the approach,CPU processing  and the way the program can be solved in different techniques using different methods.\n",2.5
-24,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis because there are multiple solutions to a problem and we need to identify the best solution according to given resources (like time and space available).\n,2.5
-25,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",For better solution and to know the Time complexity of algorithm .,2.5
-26,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To find the time and space taken by an algorithm so that we can find the most optimal solution for real-life problems.,2.5
-27,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To find optimal solutions to problems in the least time and space so that a program runs efficiently. ,2.5
-28,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To find an optimal solution to a given problem and analyze the time and space complexity which further gives us an idea which algorithm is better for a particular problem.,2.5
-29,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to analyze the algorithm to find that is the algorithm optimal and efficient for the given condition. As we know that different algorithm is suitable for different problem and analysis of the algorithm tell us the time complexity space  complexity for better analysis.,2.5
-30,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to find a better solution to solve a problem in terms of time complexity and space complexity: to solve a problem faster and by using minimum memory space possible.,2.5
-31,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to break the problem into smaller subparts and to minimize the complexity of time and space .it gives the optimal solution in the specified condition.,2.5
-32,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to solve given problem in minimum time and with less space requirement. we always try to solve problem in efficient way so that our output should be fast. for this we have to look after a given problem in many ways. suppose we solve any problem in n^2 then we try to solve the same problem in lesser time. for that we need different algorithm.,2.5
-33,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need to do algorithm analysis to check whether a algorithm is effective or not for a given problem and to know that algorithm is a time efficient, and to know how much space a given algorithm is going to take .",2.5
-34,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We do algorithm analysis to find the process that give us the solution is the best time complexity and space complexity .The lower the time and space complexity the better the solution will be this is the reason to do algorithm analysis.,2.5
-35,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need to do Algorithm analysis \n1. to identify in which time complexity we can solve the problem. Basically we solve the program with that algorithm which takes less time or whose time complexity is less than all the other approaches or method by which that problem can be solved. \n2.  To classify or standardize some problems that this problem can be specifically can be done by this method .\nFor ex; a. whenever there is a sparse matrix we apply Kruskal's  Algorithm\n            b. to find shortest path with multiple source we apply Ford Fulkerson Algorithm\n            c. to find shortest path with single source we apply Dijkstra's Algorithm , etc.  \n ",2.5
-36,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis helps us to analyze the program's complexity. Using which we can optimize the program for less time and space complexity. Which will make the program run faster than before  or use less space than before.,2.5
-37,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis so that we can know which how algorithm can be optimized further and we can know its time and space complexity.,2.5
-38,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to make the processes easier and to make these processes to work in a proper way. It also defines the Time and Space complexity of a program which shows that how much time and space the program can take . So according to it we decide that what kind of algorithm should be used in it.,2.5
-39,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",The need to do algorithm analysis is to decrease the time complexity for solving a particular problem. Algorithm analysis decreases time and space complexity of a program which heals the system to complete the program efficiently.,2.5
-40,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To solve the problem correctly and accurately by taking the less time and space. For choosing the best possible algorithm for solving the problem.,2.5
-41,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to analyze algorithms to check the time and space taken by it to solve the problem and to improve it.,2.5
-42,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to find out the appropriate and most efficient method to solve a problem with respect to time and space consumed.,2.5
-43,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm helps us to solve the problems more efficiently and quickly. It helps us cover all the use cases of the problem and eventually coming up with the best of best of solution. It saves our time, resources, and energy and also helps us achieve efficiency.",2.5
-44,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm Analysis is necessary to understand the working of an algorithm and learn about its time and space complexity. To identify which algorithm gives an optimal solution in less time. To differentiate between two algorithms used to find out the same thing and find which one of them is more efficient.  ,2.5
-45,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","For the efficiency of doing a task according to different characteristics, we need to do algorithm analysis.",2.5
-46,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to check that the algorithm which we are defining or structuring for a particular problem is not using excessive space to perform a simple task or not taking excessive time to work. Through analyzing various algorithm, we can find the best solution to the problem. ",2.5
-47,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis in order to compare the different algorithms used to solve a particular problem based on their time complexity and spce complexity.,2.5
-48,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis so as to make the program more optimum in respect of time and space complexity. ,2.5
-49,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis in order to understand the time complexity of our codes in order to make our applications run faster, and be more optamised",2.5
-50,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis because to find out the time complexity and space complexity of the particular algorithm. Also to find out that the algorithm gives correct solution.,2.5
-51,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is required so that we can check whether the algorithm we designed is efficient one or not. It is required to keep a track of time complexity as well as space complexity so that the algorithm is designed within the time frame and space given by the user along with desired inputs and outputs.\nEx: If the user has asked to solve the problem in time O(log n) then we need to check for the loops and other things to see whether it is in that time or not. This can only be done if we analysis our designed or used algorithm.,2.5
-52,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Need for algorithm analysis is used to check if algorithm is efficient or not. Algorithm should be time and space effecient.,2.5
-53,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.", To discover its characteristics in order to evaluate its suitability for various applications or compare it with other algorithms for the same application.,2.5
-54,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis for understanding how each algorithm differs with respect to space and time as we have to calculate each algorithms time complexity and space complexity and compare them accordingly  by calculating the average time complexity and worst case .,2.5
-55,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis so that we can save time and memory by developing a faster and efficient code that does the operation in least time and uses less resources.,2.5
-56,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to compare different algorithms and decide which one is more optimal and useful than the other.,2.5
-57,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","for a particular problem statement we can get various approaches to reach the solution but to get the optimized and perfectly elastic solution we analysis,",2.5
-58,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to get the optimal solution. We make sure that the algorithm we use gives complete solution and also keeping in mind the time and space complexity. ,2.5
-59,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to choose the optimal method according to our needs. Whether we need less time, less space, we can get an understanding of all the parameters using algorithm analysis",2.5
-60,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",1)to find time complexity\n2)to find space complexity,2.5
-61,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",to find most efficient algorithm that uses least resources and takes less time ,2.5
-62,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to get the efficient code so that it takes minimum time, space and easy to understand with the most efficient use to us",2.5
-63,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",When we large of data and we want to perform the operation then we need to analysis and understand which type of algorithms will work that set of data  and perform our operation faster.\n,2.5
-64,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to obtain optimal space and time complexity.,2.5
-65,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to find out that how much time a algorithm is going to take to get processed completely whether in its best, average or worst case and compare between different algorithms to find out which one is best .",2.5
-66,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is very important in case we have insufficient and lacking algorithms. We need to analyze the algorithms to come up with optimal solutions and check for use of any such lacking algorithm.,2.5
-67,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need to do algorithm analysis for finding the algorithm i.e efficient, save time and space complexity ",2.5
-68,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",\nPredict the behavior of an algorithm without implementing it on a specific.\nIt is not possible the prdict the behaviour of the algorithm.\nTo solve a complex problems with the help of the algorithm .,2.5
-69,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis for a efficient a program which would take less space and work in the fastest time possible .,2.5
-70,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Different Algorithms use different times and take up different amounts of space to solve the same problem. Some algorithms take more time to solve a particular problem than others. The objective of algorithm analysis is to find the optimal algorithm for a particular problem or set of problems that solves it in the least amount of time and takes the least amount of space possible.,2.5
-71,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis tells us about the algorithm's efficiency, its space usage, etc. This helps us to compare the algorithms and choose the best one depending upon the case.",2.5
-72,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",to make the programs more efficient and fast for the end user. it should provide as seemless experience as possble.\n,2.5
-73,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis so that we can optimally use the resources of the system and can carry out the operations in an efficient way.,2.5
-74,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is necessary for a code because every problem has different kinds of solutions, and each solution has it own specialities\nby doing this analysis we get to know that which solution to the problem suits best to what we desire as a result.",2.5
-75,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to find the optimum solution of a problem which has least time and space complexity.,2.5
-76,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is one of the important aspect to judge the specific algo with others to choose the best suited one.\nNow this include majorly few things first major aspect is the time complexity and the other one is the space complexity . ,2.5
-77,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to see the time/space complexity i.e., the amount of time or space an algorithm is going to take. It is essential to determine this so that we can use the most optimal algorithm.",2.5
-78,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to find the least time complexity so that we can write an efficient code to solve a particular problem. We can also find the comapre two alogrithms before even running it on the compiler,2.5
-79,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To predict the behavior of an algorithm without implementing it on a specific computer. It is much more easier to have simple measures for the efficiency of an algorithm than to implement the algorithm and test the efficiency every time a certain parameter is passed.,2.5
-80,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","because some algorithms may be correct but take a lot of time or space. this also could end up in failure of the code. to avoid this, we analyze algorithms to make sure to run efficient algorithms only",2.5
-81,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analalysis is helpfull in case of where we want greedy answer or faster answer in lessder timew or..we want optiumal solution of various problems ,2.5
-82,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","In order to find the best possible algorithm/solution that keeps in mind for the time and space complexity for a given problem, we need to do algorithm analysis.\n",2.5
-83,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To check the efficiency as efficient algorithm can directly affect system performance.It might take more time or more space than the required time and space which might lead to more hardware which wont be cost efficient too or it can lead to system failure too,2.5
-84,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to understand and implement algorithms in a better and improved way and suggest the necessary changes required.,2.5
-85,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis so that our proposed algorithm would work more efficiently on the basis of space and time,2.5
-86,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to analyse algorithm to frame  a suitable working complied running code which throws no error,2.5
-87,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",to learn about it's space and time complexity and use memory space efficiently and improve program's performance.,2.5
-88,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm is the basic implementation of the code.\nAlgorithm Analysis is needed because it makes easier for programmer to code if we have develop a algorithm or the basic implementation for the required output.,2.5
-89,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Because the accidental use of an inefficient algorithm can significantly impact system performance.,2.5
-90,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis provide theoretical estimation for required resources of an algorithm to solve.,2.5
-91,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to solve our problem more efficiently and optimally. It reduces the error and helps to reach our goal within required time complexity. ,2.5
-92,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do Algorithm Analysis to analyze the time and space complexities of a program. The lesser the time and space complexities the better the program works.,2.5
-93,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",For getting optimal analysis of the algorithm for getting better time and space complexity.,2.5
-94,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Because when we attempt to solve a particular type of problems all of then use same type of conditions so we make a algorithm to make it easier to understand a type of approach to a question. ,2.5
-95,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need a algorithm analysis to efficiently perform and find a  optimized solution in terms of time and space.,2.5
-96,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is required to compute the efficiency of any algorithm in its best case, worst case or average case. This also gives us an idea about which algorithm is best suited for performing a particular task.",2.5
-97,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is basically steps which we write after looking into the problem\nthrough the step wise description it become better to understand and deal with the problem,2.5
-98,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to find the efficient algorithm for  a particular set of problems, that would satisfy the required time and space complexities.",2.5
-99,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis so that we get the information about the time and space complexity so that we can reduce this and make better algorithms,2.5
-100,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To analyze the time and space complexity of an algorithm to optimize them and make them efficient so that they run faster and with fewer resources.,2.5
-101,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","To know how much work our program is doing without algorithm and \nto make it efficient using different algorithms to reduce the work,\nto make our program optimal and in better time complexity and space analysis.",2.5
-102,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithms Analysis is important because there are various algorithms for a problem\n and to differentiate when one is faster(time) and require less memory(space),2.5
-103,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","when we need a code which is well structured , easy to understand with a better time and space complexity , algorithmic codes comes handy to us. we analyze so as to interpret what the code is trying to tell us , there are plenty of algorithms which has numerous applications , in order to understand all this , we need to analyze the algorithm. ",2.5
-104,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis because the accidential or unintentional use of an inefficient algorithm can significantly impact system performance. It gives the best efficient solution which takes less time and less space.,2.5
-105,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",so that we can have information related to time taking space taking of an algorithm...so that we can use particular algorithm as per our need.,2.5
-106,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is need to get the time and space complexity of the algorithm .,2.5
-107,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","To check for the time complexity, and space complexity we do algorithm analysis. We need  algorithm to solve the problem in lesser time and the code should be memory efficient.",2.5
-108,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to understand the working and functionality of a particular algorithm. We can understand the time and space complexities by analysing the algoritm.,2.5
-109,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis because it helps us to understand which algorithm is better and more efficient.,2.5
-110,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to optimize our algorithm to find the solution with  best time and space complexity ,2.5
-111,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to find out the best possible solution for a problem. Best possible solution would be with least time and space complexity. ,2.5
-112,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to analyse algorithms for the better performance of the code logic.it is necessary for optimizing the algorithms and comparing two different algorithms and for improving  efficiency of code.,2.5
-113,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm Analysis is an essential habit while coding as it allows solving of a problem with a critically well-optimized code. ,2.5
-114,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to compare the run time and space occupied of two different approaches to solve the same problem so that we can choose the better algorithm.,2.5
-115,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",In order to get an idea about the time and space used by the algorithm and further optimize the algorithm.,2.5
-116,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","it's essential  as the unintentional use of an inefficiency in  algo  can  impact system performance ,In time sensitive code algo taking too long to run useless. ",2.5
-117,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is needed to compute the complexities of a given segment of code so that a problem could be solved using the best algorithm possible.,2.5
-118,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is necessary because it helps in analyzing the the time taken and the space occupied by the program .After analyzing we can optimize the code to ensure that the same work is done faster and occupy less space, which helps in cost cutting for an organization.",2.5
-119,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to determine given an input size the time it takes to execute the program (time complexity) and the space it consumes to executes (Space complexity).\nAlgorithm analysis helps us to compare different algorithms and their space and time complexity.,2.5
-120,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","The analysis of algorithms tells the some interesting thing about the algorithms in terms of performance. If we analyze the performance of the algorithm then we can say that the algorithm user friendliness, modularity, security, maintainability, etc. And also we can predict the speed and problem solving capability of algorithm.\n",2.5
-121,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is important to analyze the algorithms in terms of their working, time complexity and space complexity. There are many algorithms, so to choose where to use which algorithm, we have to analyze their working and complexities.",2.0
-122,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is necessary in order to choose the most appropriate algorithm according to the problem. Analysis of algorithm is done on the basis of time complexity, space complexity etc.",2.5
-123,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to do space optmization , to see that different algorithm time complexities and to compare which one is the best",2.5
-124,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to understand the time and space complexities of an algorithm so that we can find the efficiency of a given algorithm.,2.5
-125,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need to do algorithm analysis to check the time and space complexity of an algorithm, which will help to optimization of any code for better performance.",2.5
-126,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do the algorithm analysis because :\n1. To predict the behaviour of an algorithm without applying it on specific computer.\n2. It is so much convinient to have simple measures for the efficiency of an algorithm than to implement the algorithm and test its efficiency. ,2.5
-127,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithmic analysis provide us a way to optimize the techniques used to solve problems on the basis of  time spent and space leveraged to solve that problem particulary called time and space complexity. It fastens the process which can be seen effective on a large scale application.\n,2.5
-128,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is required to analyze the time complexity and space complexity of different algorithms which are designed to perform a particular task so that we can analyze and compare them to find the most efficient algorithm with minimum cost(space and time) which will eventually makes the code more efficient.,2.5
-129,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis because algorithm takes time and space so we need to check weather the algorithm take long time or small time .Better the time complexity better is the algorithm as well as better the space complexity better the algorithm.,2.5
-130,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is needed to identify the time complexity and auxiliary space spent by the algo. Hence by analysis we can make an efficient algorithm.,2.5
-131,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to-\n1.) Take in account the time and space required to process with the current algorithms\n2.) Compare the two different algorithms and realize the importance to use one of them\n ,2.5
-132,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",,2.5
-133,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","To find the optimum solution using a particular algorithm in a certain time, so to solve and find the best possible solution in best possible time.",2.5
-134,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To predict the behavior of an algorithm without implementing it on a specific computer.\nIt is much more convenient to have simple measures for the efficiency of an algorithm than to implement the algorithm and test the efficiency every time a certain parameter in the underlying computer system changes.,2.0
-135,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to use algorithm analysis to find the solution briefly and in less space and less time,2.5
-136,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to conclude whether it is an efficient algorithm or not and if it will get the job done in the available time and space. We also analyse it so that we can find the shortcomings of the algo and improve it by reducing computations wherever possible.,2.5
-137,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis helps to find how much time and space an algorithm will consume to run the program so based on this we choose better algorithm whose time and space complexity will be less and will do the same work in minimum space and time.,2.5
-138,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to check the running time and space used in the code inorder to understand the working of the code,2.5
-139,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to reduce time and space taken to implement an algorithm or to do it more optimally.,2.5
-140,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis to compute how much time the algorithm takes in execution and how much space it occupies in memory,2.5
-141,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is required to form the most optimized solution to the problem in order to achieve best possible space and time complexity of the solution.,2.0
-142,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",to effectively run our program in optimum way by requiring less time and space utilisation.,2.5
-143,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is needed to know the space and time complexity of that algorithm and to compare between algorithms to find  out which algorithm is better  and  more efficient . ,2.5
-144,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","An algorithm is a list of steps to perform a particular task. When solving problem that require mathematics or some particular logic then it is important that we make sure the that process does not use a lot of space on the system and a lot of time. Every algorithm we create has a time complexity and a space complexity that help the user decide or approximate how much time the algo will take. In order to make the systems more efficient, solve problem quicker all the while using less amount of space we need to do algorithm analysis.",2.5
-145,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithmic analysis because accidental and unintentional use of inefficient algorithm can significantly impact system performance.,2.5
-146,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis because an algorithm gives us the exact idea about what we are going to do in our program or we can say that what is the approach of solving the problem .,2.5
-147,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To know how the  algorithm is working and to compare between more algorithms on basis of various parameters like time and space complexity.,2.5
-148,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We perform algorithm analysis before we write the code in order to analyise the particular steps required in order to solve the particular question and make them more efficient in terms of complexities ,both time and space.",2.5
-149,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis helps in realizing the time taken and space utilized while obtaining a solution to a problem so as to reach the maximum level of optimization while solving a particular problem.,2.5
-150,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to get the most optimized solution of a problem to achieve best possible time complexity and space complexity.,2.5
-151,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To analysis the behaviour of that algorithm with large inputs. As we want to identify how much time and space our algorithm will take when it is given large inputs.,2.0
-152,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to fnd out which way of solving a particular problem is better.,2.5
-153,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis so that  we can find the best algorithm with minimum time and space complexity. Then further make the whole code using the algorithm.,2.5
-154,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","As we know that we can write brute force approach for any question but this will consume a lot of memory and space. But we know that in real world scenerios,  the memory and space are very costly and hence we need to do algorithm analysis. By doing algorithm analysis, we can find the time and space complexity of the given algorithm, also it become very easy for us to find how our algorithm will work in worst case and average case and hence it will become very easy to compare any two algorithm on the bases of space and time. And hence we can opt the best algorithm according to the requirement of real world. For example, We know that we have different type of string matching algorithm, So if our memory is not costly then we can use suffix tree approach and if the memory is costly then we can use another algorithm like rabin karp, naive string matching algorithm etc. So it become possible only due to the complexity analysis that we choose the best algorithm.\n\n",2.0
-155,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to measure various parameters about the algorithm like time complexity or space complexity so that we can compare different algorithms to choose a better one if possible. ,2.5
-156,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis for calculating the efficiency of a program and how much space and time it will accquire.,2.5
-157,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis to find the solution by most efficient way in terms of time complexity and space .,2.5
-158,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is necessary because using and inefficient algorithm can make our system slower and outdated.  ,2.5
-159,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to bring efficiency in our code. We also use it for resource management. Algorithm analysis is used to find the most efficient way of doing a task which is assigned to us which is by reducing the time taken by a code to give output. It also reduces the memory bits taken by a code. ,2.5
-160,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is required to solve a problem efficiently, using proper steps to save time and space.\nUse of a proper algorithm enables us to give up-to-date solutions to a problem.",2.0
-161,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",because the accidental use of the algorithm can result in change in the system perfomence either in a good manner or bad.\n,2.0
-162,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To see effectiveness of the algorithm and how much time or space it uses. This helps us see the tradeoff and decide which algorithm we should choose to solve a particular problem. Other parameters like hardware can affect the working of the code as well. ,2.5
-163,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to guess how an algorithm will behave without actually implementing it on the system . Also , by analyzing different algorithms, we can compare them to determine the best algorithm needed.",2.5
-164,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is as it helps programmer which algorithm is most efficient and best suited for a required problem. it also helps in reducing computation time.,2.0
-165,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis because it provides theoretical estimation for the required resources of an algorithm to solve a specific computational problem. ,2.5
-166,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",The analysis of Algorithms  improves the efficiency of the program and states the time and space complexity of the code.,2.5
-167,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is important in calculating complexity theory that provides accurate result for a particular problem to solve a particular logical problem .therefore analysis is required to calculate how efficient it is in terms of time and space by calculating time and space complexity.,2.0
-168,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is to be done because we need to find the best solution to our problem so that it can be solved fast and effectively without taking much space on the user's system or any much of his valuable time ,2.0
-169,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","the use of algorithm analysis helps us in minimizing the time and to understand the it better and to understand and perform suggested improvements, it also helps in maintaining a good time complexity by which our system and our code can work efficiently together",2.0
-170,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need Algrithm analysis to make the codes more efficient and easy to umderstand.It helps in simplification the learning of the codes.,2.5
-171,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need to do algorithm analysis because we need to know the behavior of the program before we implement it to a computer, since it is very hard to predict the behavior of algorithms.",2.0
-172,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm to find any solution briefly and less spacing with less timing.,2.5
-173,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",algorithm analysis is important because inefficient use of sufficient algorithm can affect system performance. It can cause or affect the complexity or performance of the system.,2.0
-174,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","to determine the efficiency of an algorithm, that is how much time and space does and algorithm take in its operation",2.0
-175,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need it to find out time complexities of different algorithms to see which one gives the solution in the lowest amount of time,2.0
-176,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to find wether the algorithm actually helps in solving the problem in most efficient way.,2.5
-177,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to study how much time and space required algorithm performs the task associated . It helps us analyse how effective is a particular algorithm when it comes to best case , average case or worst cases of a particular problem.",2.5
-178,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is an key part of complexity theory which provides theoretical knowledge for the required resources of an algorithm to solve a specific logical problem.,2.0
-179,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",the analysis of algorithm helps us to unterstand it in a more better way and also help us to make it more improved i.e. making your program more faster and efficient,2.0
-180,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do analysis to find out how efficient the algo is how much time and space it is taking so that we can compare the different algos in terms of space and time.,2.0
-181,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is required so that that user can completely understand the program and identify all the base cases which are applied in the code.\nProgrammer can also find the time complexity of the program by doing the algorithm analysis.\n,2.0
-182,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need algorithm analysis to run the given question's program in better way, better time complexity and better space complexity.",2.5
-183,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to analyze algorithms in order to find the best possible algorithm suited for our problem without actually having to implement it on the computer. Having certain measures to analyze the algorithm saves considerable time than implementing the algorithm everytime a system variable changes.,2.0
-184,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to know the running time complexity of the code as it is a vital factor when the size of the input is very large and also to know about the space it occupies\nto execute the code.,2.5
-185,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis because of following points\nTo understand use of which data structure is used in that algorithm.\nTo understand its time complexity and memory required \nTo further optimize the algorithm  ,2.5
-186,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis because it helps to get knowledge of time and space complexity ,2.5
-187,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to evaluate how an algorithm functions and how optimized an algorithm is to solve any given problem and the use appropriate data structure and algorithm gives us the most efficient space complexity and Time complexity which is possible for any algorithm as such.,2.5
-188,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Algorithm analysis is important in practice because the accidental or unintentional use of an inefficient algorithm can significantly impact system performance. In time-sensitive applications, an algorithm taking too long to run can render its results outdated or useless.\n",2.0
-189,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Analyzing various algorithms helps us in understanding and building the efficient and optimum solution to the problem statement. It helps us in understanding the uses of data structures, how we can utilize them to solve the questions. The time complexity and space complexity can be compared to understand the versatility of our code.",2.0
-190,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is now days very important to solve a particular problem in very efficient time and space.\nEvery problem can be solve through many algorithms/methods but we can get the most efficient solution by only analyzing the different algorithms. ,2.0
-191,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis to find the time/space taken by the algorithm, to find what can be done to improve complexities and to propose a alternative better solution in future. ",2.5
-192,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algotrithm analysis to check which is the best suited algorithm for the given problem.Since,every problem is different,has different constraints and requirements,different data structures are required for each and to figure out which will be the best suited for the given problem,we need algorithm analysis.We also need to check which approach has the least time and space complexity so as to produce the most optimal result.",2.5
-193,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis is required to analyze the space and time complexity of an algorithm and to understand the logic so that we can check if any better solution can be there or not.,2.0
-194,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We do algorithm analysis to compare algorithms and chose the one which is the lightest on any software or hardware. This ensures compatibility and optimization for as many devices by reducing the complexity to run the algorithm (time complexity and/or space complexity).,1.5
-195,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis to know how much space and time it will be using is it a efficient one or not,1.5
-196,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",to verify if a optimal solution is there to do the question,2.0
-197,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need algorithm analysis to ensure that the algorithm we have gives the best optimal solution , with best time and space complexity. Algorithm analysis also tells the cases in which an algorithm fails and how to solve that case. Once the correct algorithm is obtained the code can be written easily.",2.0
-198,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",to find its time and space complexity.,2.0
-199,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis in order to calculate the time and space requirements of the code/algorithm we are using.,2.0
-200,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need algorithmic analysis to find a solution which occupies least space as  well as having least running time complexity(both time and space optimsed solution),depending upon the problem,to deal with it and provide an effective solution.",2.5
-201,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We do algorithm analysis to search for the best possible solution to a problem, to optimize a solution, We can find least run time (time complexity min)",2.5
-202,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis because we have to find most effecient solution for a problem we will take less time and space.,2.5
-203,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we use algorith for making problem easy and less time to doing it.,1.5
-204,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","to find out the algorithms that are more efficient in terms of the occupied space and running time (considering, multiple solutions are available for the same problem).",2.5
-205,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","Every algorithm take certain amount of time, hence algorithm analysis is important to check whether a certain algorithm can be performed in less time or not. \nMore than time we also want to check and reduce the space complexity of the algorithm. Hence to make an algorithm works efficiently we want to reduce the time and space complexity.",2.5
-206,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To obtain most optimized solution for a given problem,2.5
-207,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",To obtain the most optimal solution for a problem which is most time efficient and space efficient.,2.5
-208,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do Algorithm Analysis because it helps us to know how efficient our program is if we do analysis of our algorithm it may help us to generate efficient time and space complexity for our program ,2.5
-209,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis because accidental use of insufficient  or wrong  algorithm can effect the system,2.5
-210,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm  analysis to make our code efficient and less time consuming. There are different ways to achieve a particular end result but certain defined algorithms make them more efficient and provide better end user experience.,2.5
-211,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis in order to optimize the code in terms of its time complexity and space complexity so that the algorithm runs in a more efficient way.,1.5
-212,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need algorithm analysis so that we can check the utility of an algorithm in a particular problem, that how much is it useful. these are done by analysing the space and time complexity of the algorithm",2.5
-213,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to know the efficiency of the algorithm on the basis of different parameters such as time and space. this analysis is used to compare different algorithm and see which algorithm is best suited for the given task and its requirement. ,2.5
-214,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis to understand the proper functioning of the program we intend to develop. Algorithm analysis also gives the ability to use the most efficient algorithm keeping the desired requirements intact to give best system performance.,2.5
-215,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis defines how efficient an algorithm or the program is.  It describes the least and max time and space taken by the program. ,2.5
-216,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need algorithm to make our code very efficent and also take less timing ,take less memory in algorithm we can solve one question in many ways .\n",2.5
-217,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need algorithm analysis so that we can figure out the best and most optimised algorithm to solve our problem. Each algorithm takes certain space(memory) and certain running time. Because we want to complete our work using minimal memory and time, algorithm analysis is essential.",2.5
-218,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis to determine the running time and space time complexity of  the algorithm,2.5
-219,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",it is important because the accidental use of an inefficient algorithm can be effect system performance,2.5
-220,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to find the best approach to a particular type of problems statements.,2.5
-221,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis in order to determine the time and space complexity of the algorithm and to know when the algorithm is taking the best time and when it is taking worst.\nIt also tell us how much space we need in order to implement that algorithm.,2.5
-222,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","To prepare a blueprint before the execution of actual program and to generalize a problem statement , we need to do algorithm analysis.",2.5
-223,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis because it  helps us to understand the process of any algorithm step by step to understand the time complexity of any problem.,2.0
-224,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis because to optimism \n the code and save the runtime time complexity  and space in the code,2.5
-225,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis allows us to create a preview of the working of the algorithm thus determining the time and space complexity beforehand as a rough sketch and also if the algorithm is functioning properly and as desired or not.,2.5
-226,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algorithm analysis to find there running time complexity and space complexity,2.5
-227,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis  is use to  give a basic structure  to program or precise the code.  ,1.0
-228,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis to know the time and space complexity of algorithm and also find the best way to solve the problem. W analyze the algorithm to find its benefits and drawbacks. of used algorithm.,2.5
-229,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do analysis of algorithm in order to find efficient solutionn as per given complexity,1.5
-230,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need algorithm analysis for performing specific task through best algorithm in order to find optimal algorithm we compare and imply changes in specific algorithm to achieve optimal algorithm in terms of space and time complexity.,1.0
-231,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we use algorithm analysis so we can shorten the time complexity and space complexity of a particular code. So to save the cost of computing in the future.,1.0
-232,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","algorithm analysis is needed to know about the program efficiently and acknowledge the complexity and details about the problem, to provide the solution efficiently. ",1.0
-233,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis in order to practice the coding for program for the given problem,0.0
-234,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","We need to do algorithm analysis to get an idea of and determine which algorithm would take the most optimal amount of time and memory space. We do this because , both time and memory are limited in nature and using it efficiently hence becomes important for the developer",2.5
-235,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",We need to do algorithm analysis to solve the problem statement into sub problems.,0.0
-236,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","it is because by algorithm analysis , we will get to know what is the procedure and approach that we have applied to solve particular problem.\nbasically a step by step approach towards given problem.",0.5
-237,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we use algorithm analysis to solve multiple set of problem by the methodof travelling salesman problem , coin change etc.",0.0
-238,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithm analysis mainly to find out about their time and space complexity.\nspace complexity is the memory/storage used up by the algorithm.\ntime complexity is the time taken by the algorithm to complete.,2.5
-239,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need algo analysis to optimize the the performance of our system for getting a desirable output.,0.0
-240,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",Algorithm analysis helps us keep in track with the time and space efficiency of our code.,1.0
-241,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",because analysis is need time min and maxing time optimization's and storage optimization or resource optimization,1.5
-242,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",find easy method to the problems.,0.0
-243,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.","we need  algorithms for solving day to day problems like traveling salesman problem ,coinage problem etc and have a better understanding about data structures ",0.0
-244,Why we need to do algorithm analysis?,"A problem can be solved in more than one ways. So, many solution algorithms can be derived for a given problem. We analyze available algorithms to find and implement the best suitable algorithm.",we need to do algorithms analysis to determine the appropriate way to deal with the problems given. ,2.0
-245,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",We determine the efficiency of an algorithm on the basis of it's time complexity and space complexity.,2.5
-246,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","BigOh(theta), bigoh(omega), smalloh(theta)\nasymptotic analysis ",1.5
-247,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","its input should be given , output should be specified ",1.5
-248,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","There are two basic criteria for algorithm analysis: 1) Time Complexity; 2) Space Complexity. Time complexity measures the amount of time taken by a particular algorithm, whereas space complexity measures the space taken by the data structures used in the algorithm",2.5
-249,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","We consider time complexity, space complexity and the various data structures that are required with a particular approach. In time complexity we check how the time taken by the algorithm changes with the size of the input data. In space complexity we check how much space is used by the algorithm to store values and how it uses the stack memory.  ",2.5
-250,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","There are criteria for algorithm analysis like time and space.\nWe tried to find algorithm complexity like in best ,worst and average case .\n",2.5
-251,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",all algorithm must satisfy the following criteria :\n zero or more input values .\none or more output values .\n clear and unambiguous instructions .,1.5
-252,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of algorithm is time and space.,2.5
-253,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria for algorithm analysis is that the algorithm should do the required operation in minimum time and space .,2.5
-254,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1. understand and analyze the program carefully\n2. work on how to approach the program in a particular way\n3. find out the data structures which would execute the code in the most efficient way\n4. combine your approach along with the data structures to develop the code,1.5
-255,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of given problem is-\n1. study the given problem and to analyze it .\n2.prepare a approach for the given problem.\n3.find the data structure which is most suitable for the given problem.\n4.write your approach .,1.5
-256,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria's are :-\n1) Time complexity\n2) Space complexity \n3) Optimized solution approach\n4) Optimum answers ,2.5
-257,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","First is to understand the problem, and generate the basic idea nd solution for it.\nWriting the solution an ideology in statements and then implementing it  through code.",1.5
-258,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",We judge any algorithm based on the amount of time it takes to execute a task and the amount of space it requires to do the same. Thus the TIME and SPACE COMPLEXITY are the two criteria.,2.5
-259,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",following are the criteria for algorithm analysis:\n-time complexity\n-space complexity,2.5
-260,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.", Space and Time Complexity.,2.5
-261,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1. start by first analyzing the loops since the loops take the maximum time \n2. try to reduce the number of approximations you take\n3. don't leave out any piece of code while analysis\n4. ,2.0
-262,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis are as follows:-\n1. Code must compile and run successfully.\n2. We must know all the basic technical terms and procedure of calculating it.,1.5
-263,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Algorithms can be detected using the space complexity and time complexity ,whether it covers all the cases in best efficient manner.\nthe shortness of code also helps to reduce cache",2.5
-264,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of algorithm analysis are time and space complexity,2.5
-265,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-266,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-267,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria for algorithm analysis is to check time complexities,space compllexities ,to check the optimisation and the algorithm is meeting the desired condition.",2.5
-268,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Algorithms that give us the solution in minimum time and acquire minimum space are better for our use. Hence, algorithms with less space and time complexity are considered more useful over those with high time complexity or space complexity. ",2.5
-269,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Time complexity , space complexity , optimised solution ",2.5
-270,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Time complexity, Space complexity",2.5
-271,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The algorithm should ,0.0
-272,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",WE need to find an optimal solution to the given problem and use the least time and space to implement our solution. This leads to finding an efficient approach.,2.5
-273,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithm are analyzed on the bases of : \n1) Time complexity\n2) space complexity\n3)  its accuracy \n4) its efficiency.,2.5
-274,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis are time complexity and space complexity.,2.5
-275,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time complexity\nspace complexity,2.5
-276,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The basic criteria is :\n1) time complexity\n2) space requirement\n3)time require to provide output  (suppose we design an algorithm that is providing output in 2 days then I don't think this is a good algorithm. we always focus on providing output in lesser time ),2.5
-277,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithm analysis is done on majorly two basis :-\n1) in terms of time :- time complexity.\n2) in terms of space :- space complexity\n ,2.5
-278,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time complexity\nspace complexity ,2.5
-279,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of Algorithm is \n1. Best Time complexity\n2. Space Complexity\n,2.5
-280,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria for algorithm analysis is that while analyzing, we should focus on wither reducing the time complexity or the space complexity.",2.5
-281,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","the criteria of algorithm analysis is its efficiency , time complexity and space complexity.",2.5
-282,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of Algorithm analysis is Time and Space complexity.,2.5
-283,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",there are two criteria of algorithm analysis: \n1. Time complexity.    \n2. Space complexity,2.5
-284,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithms are analysed on the basis of space and time complexities. A good algorithm uses less time and space combination.,2.5
-285,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Algorithms are analyzed by checking time and space complexities. It is in terms of the variables (for example n, where n is the length of the given problem).",2.5
-286,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria of Algorithm analysis lies with :\n1) Amount of time required - Few algorithms require constant time while some take exponential time. The prior takes less time , hence more efficient.\n2)Space consumed - Few Algorithms require more space while others take constant space. The latter works more efficiently in comparison as they allow space for other  algorithm to work simultaneously accessing the remaining spaces. ",2.5
-287,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","While analyzing the algorithm, you should take care of the time complexity, efficiency,  ",2.5
-288,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of Algorithm Analysis:\n- through which do we receive an optimal solution\n- less time and space complexity\n,2.5
-289,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are three different criteria for the algorithm analysis:\n(i)Time basis\n(ii)Space complexity\n(iii)consistency,2.5
-290,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria for algorithm analysis:\n1. Time complexity\n2. Space complexity,2.5
-291,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Criteria in order to analyse a Algorithm are as follow:\nTime Complexity, Space complexity.",2.5
-292,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is that the program must be executed within minimum time and space complexity.\nThe program should be most optimum one in respect of time and space complexity.,2.5
-293,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The basic criteria of algorithm analysis  are: time should be minimum\n                                                                      space should be minimum\n                                                                      program should achieve the given task,2.5
-294,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is that the time taken should be minimum and space complexity should be minimized.,2.5
-295,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Algorithm analysis has following criteria: \nIt should be time and space efficient i.e it should be less time and space consuming. \nIt should take a finite input and then generate a finite output.\nIt should not produce infinite output.\nIt should always produce the desired output in a finite amount of time. \nIt should be language independent as well.,2.5
-296,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Algorithm should take finite time to complete,\nit should take finite space\nit should take finite input from user and\nshould return finite output.",2.5
-297,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","An algorithm is a finite set of instructions that, if followed, accomplishes a particular task. \nAll algorithms must satisfy the following criteria:\nInput. An algorithm has zero or more inputs, taken from a specified set of objects.\nOutput. An algorithm has one or more outputs, which have a specified relation to the inputs.\nDefiniteness. Each step must be precisely defined; Each instruction is clear and \nunambiguous.\nFiniteness. ",2.0
-298,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the algorithm should have zero or more well-defined inputs.,1.0
-299,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algorithm analysis is time and space complexity. The algorithm having least time and space complexity is fastest and most efficient.,2.5
-300,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are two criteria of algorithm analysis - time complexity and space complexity. Time complexity refers to the amount of the the computations take in an algorithm whereas space complexity refers to the total memory which all the data structures in an algorithm occupies during it's execution.,2.5
-301,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","getting the optimal solution for a problem where by the solution obtained is optimized in time and space complexities,\nthereafter for some approached like greedy, the solution may or may not be optimized but it is simpler to understand an goes with the flow",2.5
-302,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Firstly, the algorithm is written to get the solution of a particular problem. Also check if the solution we get is complete and optimal. \nSecond step is to find the time required for each step and then summing them up to find time complexity. \nThird step is to find how much space the algorithm takes to solve and finding the space complexity. \nOn these basis, we analysis if the algorithm is effective or not.",2.5
-303,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Algorithm analysis include following parameters:\nTime complexity\nSpace complexity\nInplace or not\nStable algorithm or not\netc.,2.5
-304,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1)feasibility\n2)optimality,1.0
-305,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",resources used and time taken,1.5
-306,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are two of them \none is time analysis and another is data analysis,1.5
-307,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There is 2  criteria of algorithm analysis\n1.Time complexity \n2.space complexity ,2.5
-308,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithms must satisfy the following criteria:\n1. there should be finite input and outputs in a program\n2. the code should be unambiguous\n3. Each step of the algorithm must be feasible,1.0
-309,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",AN algorithm should have 0 or more values of input.,1.0
-310,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are multiple criteria for analyzing an algorithm. We can break down a given algorithm into multiple sub problems and solve the given question (divide and conquer) or find the first possible solution of the same (greedy algorithm). The approach towards a problem and the algorithm used gives rise to need for analysis. ,1.0
-311,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria of algorithm analysis is checking whether it is complete, admissible, minimum time and space complexity.",2.5
-312,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",All the algorithm have the criteria have satisfy  zero or more input values one or more output values clear  instructions. Means the value of input is zero or mpore than one.,1.0
-313,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",there are two criteria of algorithm analysis \n1) Time complexity- the time it takes for a program to run completely\n2) space complexity - the space a program takes in the system ,2.5
-314,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The following criteria are used for algorithm analysis: \n1. The time complexity\n2. The space complexity,2.5
-315,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-316,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",resources and time taken,2.0
-317,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",An algorithm is judged on the basis of its time complexity (time taken to run that algorithm) and space complexity (space occupied by that algorithm)\n\n,2.5
-318,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",All algorithms must satisfy the following criteria: Zero or more input values. One or more output values. Clear and unambiguous instructions.,1.0
-319,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1. The solution should have least time complexity.\n2. Least space complexity.,2.5
-320,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Two major criteria are\nTime complexity:\nIn order to judge the speed of the work or job done by the algorithm we need to do analysis of its time complexity.\nSpace complexity:\nIn order to judge the algo over its consumption of its space in terms of variables and data structure space complexity is must.,2.5
-321,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","there are three types of algorithms on the basis of analysis.\nbest case, average case and worse case algorithms. They are categorized on the basis of time complexity.\n",2.0
-322,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria for algorithm analysis are\n1. TIME COMPLEXITY OF THE ALGORITHM\n2.SPACE COMPLEIXTY\n,2.5
-323,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",All algorithms must satisfy the following criteria: Zero or more input values. One or more output values should be available.,1.0
-324,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time complexity\nspace complexity,2.5
-325,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithm analysis is getting optimal answer,1.0
-326,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",We assume that every statement is completed in O(1) i.e. constant time\n,1.5
-327,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","time complexity,space complexity",2.5
-328,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",All algorithms must satisfy the following criteria: \n1. Zero or more input values. \n2. One or more output values.\n3.  Clear and unambiguous instructions.,2.5
-329,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",main criteria of algorithm analysis are space and time complexities that a algorithm would take\n,2.5
-330,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.", Criteria of algorithm analysis is Time and Space complexity,2.5
-331,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",space complexity\ntime complexity\nprogram reliability\n\n,2.5
-332,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Algorithm should be in basic language so that programmer can understand it easily.,1.0
-333,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.", zero or more input values and one or more output values are the criteria of algorithms analysis. ,1.0
-334,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",All algorithm satisfies following criteria: \nZero or more input values.\nOne or more output values.,1.0
-335,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm analysis is time and space complexity ,2.5
-336,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time and Space,2.5
-337,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Space , time  complexity.",2.5
-338,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","when making a algorithm there are criteria's like base cases , when there should be  inputs,outputs and clears instructions for processing them.",2.5
-339,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",We analyses algorithm on the basis of recurrence functions it helps to gain knowledge which algorithm is best for the case ,2.5
-340,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of algorithm analysis is:-\n1. Time complexity\n2. Space Complexity,2.5
-341,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria is get a probelm\nlook into the problem\ndivide into parts and find out the ways\nthen quickly merge them out,1.0
-342,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is:\n1- Time complexity of the algorithm.\n2- Space complexity of the algorithm.,2.5
-343,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time And Space,2.5
-344,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","An algorithm can be analyzed on the basis of the time it takes to solve a problem or the space it takes in the memory stack. It can be seen on the basis of the best case, the worst case and the average run time of an algorithm.",2.5
-345,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","To get the reccurance relation to know how much time it usually and to make it work in less time complexity, \nspace it takes to run the program, how much memory it uses to run it efficiently\nand to make it optimal, in case of recursion to reduce the stack space it usually\ntakes to run recursive calls, how much code in how many lines and to reduce the work done by it makes it a criteria of analysis.\n",2.5
-346,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","There are 2 main criteria for algorithm analysis\n->TIME ,, which  one algorithms require less time to run i.e less Time is more faster algorithms\n-> MEMORY(space)  ,, which one algorithms require less space to run i.e less memory is more faster  algorithms",2.5
-347,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",it should have lesser time complexity \nspace complexity should be minimum\nalgorithm should be well structured and easy to understand.,2.5
-348,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is that zero or more input values and one or more output values.,2.5
-349,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","on the basis of time complexity(time taken by different operation like loops ,conditionals)\non the basis of space complexity(total number of characters used)",2.5
-350,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-351,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",We check for time complexity and space complexity.,2.0
-352,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Algorithm analysis is limited because of hardware restriction. We can analyse the time and space complexities of an algorithm.,2.0
-353,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria are time complexity and space complexity.,2.0
-354,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",We find the optimized solution on the basis of space and time complexity,2.0
-355,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of algorithm analysis are time and space. ,2.0
-356,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm is based on time complexity analysis and space complexity analysis,2.0
-357,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1) Time Complexity\n2) Space Complexity,2.0
-358,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time and space complexity,2.0
-359,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time and space,2.0
-360,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The following requirements must be met by all algorithms: any number of input values. any number of output values. Unambiguous directions that are clear. ,2.0
-361,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time taken by the algorithm and the memory consumed by it,2.0
-362,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-363,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are two criterias:\n1. Space complexity \n2. Time complexity,2.5
-364,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","1. Input: An algorithm should have zero or more but should be a finite number of inputs. We can also say that it is essential for any algorithm before starting. Input should be given to it initially before the Algorithm begins.\n2. Output: An algorithm must give at least one required result from the given set of input values. These output values are known as the solution to a problem.\n3. Definiteness: Each step must be clear, unambiguous, and precisely defined.\n4. Finiteness: Finiteness means Algorithm should be terminated after a finite number of steps. Also, each step should be finished in a finite amount of time.\n5. Effectiveness: Each step of the Algorithm must be feasible i.e., it should be practically possible to perform the action. Every Algorithm is generally expected to be effective.",2.5
-365,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1. Working Structure - How the algorithm is working\n2. Time Complexity - How much time it is taking in solving any problem\n3. Space Complexity - How much space it takes,2.5
-366,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Criteria of algorithm analysis are time complexity , space complexity etc.",2.5
-367,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1. time complexity\n2. space taken by the algorithm,2.5
-368,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria for algorithm analysis are Time and Space.,2.5
-369,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1 -> speed or run time an algorithm \n2 -> how much computer space it uses while running.,2.5
-370,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The different criterias of algorithm analysis are :\n1. It should occupy as less space as possible.\n2. The run time of any program based on that algorithm should be as low as possible.\n3. The algorithm should be user-friendly.,2.5
-371,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-372,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.", time complexity and space complexity are the two criteria on which algorithms are analyzed and compared with each other .,2.0
-373,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time complexity and space complexity,1.5
-374,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis are as follows :-\n1) Time Complexity\n2) Auxiliary Space used\n\nThe aim is to minimize the above two criteria's.,2.0
-375,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The algorithms are majorly determined on the basis of space and time complexity.,2.0
-376,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-377,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-378,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","input, output, ",1.0
-379,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algorithm analysis is to check time complexity and space complexity for example we need to check that a algorithm is in a worst case or average case or in a best case by algorithm analysis,1.0
-380,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","We can analyse the algorithm on the basis of time and space. When analysing on the basis of time there are three cases to be considered: worst case, average case, best case. When talking about space we see whether it is auxiliary space or not. ",2.0
-381,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.", The criteria of algorithm analysis is that its time and space complexity should be minimum ,2.0
-382,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria to do algorithm analysis are space and time analysis by which we can compare the different methods of solving the problem efficiently,2.0
-383,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria of algorithm analysis can be time taken ,or space taken by the program, or the amount of battery is uses. It depends on the user.",2.0
-384,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algorithm analysis are posteriori analysis and priori analysis,2.0
-385,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",space and time are the most important criteria for algorithm analysis.,2.0
-386,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time complexity\nspace complexity,2.0
-387,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm analysis is to calculate the space and time complexity of the algorithm ,2.5
-388,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria for algorithm analysis when we are performing it theoretically is that we imagine that all the computers will have similar processing power and will be working under the same conditions. We need to make sure that the pseudo code we write can be run in a majority of main programming languages. ,2.0
-389,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1.Zero or more input values\n2.One or more output values\n3.Clear and unambiguous instructions,2.0
-390,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is that it should be applied to every kind of that problem and it should not give different outputs with the same input or it should give the correct output as per the input.,2.0
-391,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria is to  analyze each command of the analysis and compute its complexity.,2.0
-392,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Criteria for algorithm analysis is at first to mark definite steps to solve that particular question. Secondly, to analyise the steps in terms of complexites, both time and space in order to make it more efficient. ",2.0
-393,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",there are two parameters involved:\n1. time \n2. space,2.0
-394,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is time complexity and space complexity.,2.0
-395,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",We analysis algorithm mainly on both that is on the basis of time and space complexity.,2.0
-396,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm analysis are:\ntime it takes\nspace required,2.0
-397,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Algorithm analysis includes finding out the time and space complexity.,2.0
-398,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria to do the algorithm analysis are Space and Time. We generally do algorithm analysis on the bases of these two factors and generally find the best one according to our real world problem requirement. By doing algorithm analysis, we can find the time and space complexity of the given algorithm and hence it will become very easy to compare any two algorithm on the bases of space and time.",2.0
-399,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time complexity\nSpace complexity,2.0
-400,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria for algorithm analysis is: Use of recursion or iteration, use of complex libraries or time taking commands, code redundancy etc",2.0
-401,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithm is analzsed on the basis of time complexity and space used in the algorithm,2.0
-402,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algorithm analysis: \n\n,2.0
-403,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of algorithm analysis are:\n1. Efficiency\n2. Space and Time taken by the code\n3. Scalability and Predictability \n4. Resource Management ,2.0
-404,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria for algorithm analysis- \nThere can be zero or more inputs.\nThere can be zero or more outputs.\nThe instructions should be clear and not ambiguous.,2.0
-405,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algrothm analysis is to give one or more  input values.either zero or any other number\n ,2.0
-406,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria for algorithm analysis is mainly the space and time an algorithm uses.  Hardware and software versions the user is using and other such parameters may also affect the working. ,2.0
-407,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria for algorithm analysis is input size and time and space complexity.,2.0
-408,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria for algorithm analysis are time and space complexity. ,2.0
-409,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Pseudocode and flow chart can be used to represent an algorithm. Repetition is included in the steps of an algorithm. It is written in human understandable language.\n,2.0
-410,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time Complexity--- It checks the speed and run time of the code \nSpace Complexity--- It analyses the space occupied by the code,2.0
-411,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria for algorithm analysis is zero or more input values returning the output as one or more input values with clear language efficient time complexity and space complexity with efficient algorithm.,2.0
-412,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-413,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algorithm analysis are:\nzero and more input values\n,2.0
-414,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of the algorithm analysis is to make algos and ways to solve the paricular types of the questions.,2.0
-415,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-416,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algorithm analysis should be zero or more input values ,2.0
-417,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","the criteria which algorithm must follow are:\n1)It should have 0 or more input value\n2)It should have 0 or more output values\n3)It should have clear ,sufficient and unambigious instructions.",2.0
-418,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the main criterion of analysis of an algorithm is its time and space complexity,2.0
-419,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria must be there should be an unambiguous, fineness.",2.0
-420,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time complexity\nSize complexity,2.0
-421,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of a algorithm analysis is to measure its time complexity and space complexity .,2.0
-422,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The main criteria to analysis the algorithm is to first understand the the logic behind the algorithm and impliment it in a systematic manner.,2.0
-423,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",it should always satisfy more than 1 input and give efficient output\nit should always return the type of error being  occurred at the time of running as well as the complexity of the program,2.0
-424,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Criteria of algorithm analysis is finding least upper bound of time complexity, highest upper bound of time complexity and then finding out average case time complexity.",2.0
-425,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria is to find the best possible time complexity for the program which covers all the base cases and also find the error in the code if present.\n,2.0
-426,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",there are two criteria a algorithm analysis time complexity and space complexity.,2.0
-427,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","An algorithm must satisfy the following criteria:\nInput: An algorithm should have zero or more but should be a finite number of inputs. We …\nOutput: An algorithm must give at least one required result from the given set of input …\nDefiniteness: Each step must be clear, unambiguous, and precisely defined.\n",2.0
-428,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","run time, and space required for the code to execute",2.0
-429,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",first we analyze the algorithm based on its time complexity and then we analyze it on the bases of its memory \nand the best algorithm which is taking less time and space is considered,2.0
-430,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-431,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria of algorithm analysis basically includes analyzing the Time complexity and space complexity of an algorithm such that it takes less memory , space and time for the same. \nFor analyzing the Time complexity of an algorithm - the criteria depends on how many times each function is running in the algorithm(considering main() as a function). As each time the algorithm runs (or parts of it) the time complexity of it keeps increasing. For example - Each \",2.0
-432,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","An algorithm should have 0 or more well-defined inputs. Output − An algorithm should have 1 or more well-defined outputs, and should match the desired output. Finiteness − Algorithms must terminate after a finite number of steps. Feasibility − Should be feasible with the available resources.",1.0
-433,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Symptotic and Asymptotic.,2.0
-434,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Criteria for Algorithm analysis:\n1) Time complexity of the algorithm. We analyze an algorithm according to the time the algorithm takes, the algorithm whose Time complexity is better will be consider as the better approach to solve a problem.\n2) Space complexity is also an important criteria to analyze an algorithm.  ",2.0
-435,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The Criteria for algorithm analysis is:\n1.time required by the algorithm in best case, worst case and average case. \n2.space required by the algorithm in best case, worst case and average case.",2.0
-436,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Criteria of algorithm analysis :\n1.Time complexity:We go for the algorithm rhat has the least time comlexity.We avoid exponential complexity and go instead usually for linear or better time complexities.\n2.Space complexity:We choose the algorithm that takrd the least space and has the least space complexity.Sometimes,the algorithm having the least time complexity may not have the least space complexity and vice-versa.Then,according to the problem,we choose the most suited algorithm.",2.0
-437,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",1. time complexity\n2. space complexity,1.0
-438,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Speed of execution of the algorithm or (the code implemented using the algorithm).,2.0
-439,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm analysis is time and space complexity and develop a optimise solution . Analysis of time complexity is done with help of asymptotic analysis,2.0
-440,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time and space complexity,2.0
-441,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Some criteria's of algorithm analysis-\n1. gives best time and space complexity. \n2. gives optimal solution.\n3. does not take up a lot of space to store data.,2.0
-442,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",we do analysis to find optimal solution . analysis in done according to the updation required for a variable like increment or decrement.,2.0
-443,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is that we have to find a solution which is time efficient as well as space efficient. We do analysis according to number of updation (increments and decrements) in a variable.,2.0
-444,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","criteria of algorithm analysis is that the solution that we have derived must be better in terms of space and time complexity in comparisonn to the previous solution.It also includes the fact whether the problem can be broken down into further sub-problems or the data is associated in some way or not,etc.",2.0
-445,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","optimized solution, best case run time",2.0
-446,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is-\nTime complex\nspace complex,2.5
-447,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",I have first approach is that the algorithm . is not be large and main approach is that less time complexity in comparing to other method . For developing the algorithm we know the appropriate logic to attempting and solving the problem.,1.0
-448,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","there are 2 major criteria of algorithm analysis, time and space.",2.5
-449,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are two criteria of algorithm analysis \n1. Time complexity \n2. Space complexity,2.5
-450,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",there are 2 criteria that a good algorithm should meet:\n1.  less time complexity\n2. less space complexity,2.5
-451,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Time Efficiency\nSpace Efficiency\nOptimum Solution\n,2.5
-452,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of Algorithm Analysis is to find the time complexity and space complexity of given program \nwe can find time complexity from different methods like recursion method etc.,2.5
-453,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the main criteria of algorithm anallysis is that how fast the algorithm works we need to solve problem in quick time for that we need to design algorithms which work fast and accurately,2.0
-454,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Algorithm is analiysed mainly on two criteria : \n1) Space complexity - How much space (space in RAM taken by variables and many other things) our program require.\n2) Time complexity - How much time is taken by certain pieces of code to perform a certain operation.,2.5
-455,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria of algorithm analysis is that the analysis of algorithm should be done through its space and time complexity in order to make the algorithm as efficient as possible so that it runs in a much more efficient way and at the same time it takes as less time and space as possible.,2.5
-456,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time complexity and space complexity are the two criterias ,2.5
-457,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithm analysis generally takes place on the basis of two criteria time and space.,2.5
-458,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Algorithm analysis is done on the basis of purpose, the time complexity generated and the ease to use them. ",2.5
-459,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria for algorithm analysis: \n,0.0
-460,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",two criteria is required mainly in algorithm \n1.  space complexity-in which how much time we take to solve the\n2.time complexity- ,2.5
-461,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are two criteria for algorithm analysis:\n1. Time complexity : The criteria which tells us in which factor running time grows as size of input(n) increases.\n2.Space complexity: The criteria which tells us in which factor memory required grows as size of input(n) increases.,2.5
-462,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",a good algorithm analysis is the one in which the time complexity and space complexity is the least,2.5
-463,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-464,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-465,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",,0.0
-466,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","Algorithm analysis works on the criteria of building a solution which is generalized for same category of problems ,we describe the main function which is to be executed to solve the problem.",0.0
-467,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","The criteria to understand the algorithm analysis is delta, omega theta etc.",0.0
-468,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",to analysis the algorithm see the number of steps formed number of line ,0.0
-469,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are two criteria for algorithm analysis:-\n1. Time complexity analysis: To determine the time taken by the algorithm.\n2. Space Complexity Analysis: To determine the space used by the algorithm for the whole processing as well as generating output.,2.5
-470,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",algorithm can be analysed by 4 methods \n1) substitution method \n2) recursive method\n3) tree method\n4) master theorem,0.0
-471,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",There are some criteria of algorithm analysis:\n--> Subsitution method \n-->,0.0
-472,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","criteria of algorithm analysis are time complexity , what approach a algorithm is based.\n",2.0
-473,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",in order to do algorithm analysis we must know firstly the asymptotic notations ,0.0
-474,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Space complexity and Time complexity are the two main criteria of algorithm analysis.,2.5
-475,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm analysis is time complexity and space complexity.,2.5
-476,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm analysis is to know about the problem first and divide it into fewer subproblems and come to solution at last,0.0
-477,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",Criteria of algorithm analysis :-\n1) substitution analysis\n2) interpret analysis\n3) divide and conquer analysis,0.0
-478,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The worst case is always given priority when doing algorithm analysis\nWe should look at the complexity when the independent variable which is time is tending to infinity\n,0.0
-479,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The basic criteria of algorithm analysis is that problem must be divided into sub problems.,0.0
-480,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",like we see the time complexity and space complexity that problem has and among various thinking process and approach towards particular problem we are able to know the best technique. ,2.5
-481,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria of algorithm analysis is to make the code to algo to easy understanding .,0.5
-482,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",criteria for algorithm analysis is to minimize the time complexity and storage required to run the program.,2.5
-483,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",The criteria is to maintain the time complexity and optimize a code to get a peak outcome of the product so that the major criteria is time complexity ,2.0
-484,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria of algorithm analysis\n1) time efficient\n2) space efficient,2.5
-485,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",time and storage and resources,2.5
-486,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.","recursive function, time compexity etc.",2.0
-487,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution time and how much extra space required by the algorithm.",the criteria for analysis is that we give anylaze algs on each step and combine all the results that are griven from the indivdual solution ,0.0
-488,What are the criteria of algorithm analysis?,"An algorithm are generally analyzed on two factors − time and space. That is, how much execution��time and how much extra space required by the algorithm.","criteria of algorithms analysis is notation, searching, time complexity, ",0.0
-489,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-490,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis of an algorithm is to determine the algorithm belongs to BigOh(theta), bigoh(omega), smalloh(theta)",1.0
-491,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",its calculate the running time of the algorithm,2.0
-492,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis is used to show three basic cases of any algorithm, namely: 1) The Best Case, 2) The Average Case, 3) The Worst Case; which calculate the complexities for the best case of the algorithm, general case of any algorithm, and the worst case respectively",2.5
-493,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis is a way of representing the time complexity of an algorithm. It allows us to bound the time taken by the algorithm in 1)Lower bound(minimum time taken) 2) Upper bound(maximum time taken) 3) Average bound. ,2.5
-494,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm is a type of analysis to find the upper and lower bound of the complexity of algorithm.,2.0
-495,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm is a process of calculating the running time of an algorithm in mathematical unit to find programs limitation. ,2.0
-496,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm inform us about where the time complexity of an algorithm lie whether it is in upper bound (O) avg bound (theta) lower bound (omega),2.5
-497,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis divides the time taken by algorithm into three parts that is worst case best case and average case time,2.0
-498,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is to analyze the time complexity as well as the space complexity of the given program,2.5
-499,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",To study the complexity of a given problem such as finding the best or worst or average time complexity  asymptotic notations are being analyzed.,2.5
-500,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis is done to get the knowledge of time taking at every step of an algorithm and to  know the overall time complexity of the algorithm.,2.5
-501,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis algorithm is an algorithm which defines the ultimate answer or solutions of the program we are running.,2.5
-502,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The calculation of time taken for each statement in the program to execute based on the complexity of that statement is asymptotic analysis.,2.0
-503,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis involves the use of \nbig o \ntheta\nomega notations for knowing the time complexities of a program,1.0
-504,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis refer to determining a mathematical expression for Time and Space complexity of an algorithm. \n,1.0
-505,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis refers analyzing the algorithm based on certain parameters such as no of iteration in a loop, no of iterations in a recursive function, etc.\n",1.0
-506,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-507,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic notation are used to calculate the time complexities  in term of equal ,less, more ",2.0
-508,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis helps us to analize the algorithm using certain notations.,1.0
-509,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis is the analysis regarding time and space complexities of various cases  like best , worst and average cases in an algorithm. This analysis helps us in improving the overall efficiency of an algorithm.",2.0
-510,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic notation gives us a method for classifying functions according to their rate of growth. ,1.0
-511,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic Analysis shows the complexities of algorithm which is being solved through some particular notations,which makes it easier to know which algorithm takes more time and which is optimised. It is recognised by the functions and algorithm used in program.",1.5
-512,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of algorithm deals with the time and memory the algorithm uses to function. ,1.5
-513,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","here we analyise the lower bound of the solution time complexity , like our solution existing time complexity is O(n) then its lower bound should be also O(n)",1.0
-514,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",To find the time and space taken by an algorithm to function o different types of test cases.,1.0
-515,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The analysis of an algorithm in the form of algebraic functions to denote the time or space consumed by the program is knows as Asymptotic analysis. ,1.0
-516,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The analysis of an algorithm in the form of an algebraic function and denotes the time and space consumed by the algorithm is known as asymptotic analysis.,1.5
-517,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-518,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm means finding the time complexity of an algorithm on the basis of its recurrence relation or given algorithm using substitution method, recurrence tree method, master's theorem, etc.",1.5
-519,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-520,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The asymptotic analysis of an algorithm is to represent an algorithm in some type of notation so that we will analysis it further. we analysis the algorithm in many ways :\n1)with the help of Reccurance.  relation\n2)by plotting the graph and analysing it\n3)with the help of master theorem\n,1.0
-521,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",study of an algorithm in terms of mathematical expression and using math graph is basically asymptotic analysis of algorithm.,1.0
-522,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-523,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm means whether the time complexity of an algorithm will be in big-oh, omega , or theta. There is specific criteria to identify whether we have to take big-oh, omega or in theta. ",1.0
-524,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm refers to the examining and analysis of a program to calculate its running time. We check the time taken by the program to give an output. ,1.0
-525,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",It tells the running time of the algorithm.\n,1.5
-526,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic Analysis gives us the possible range of time that in how much time the algorithm is taking to run the program.,1.0
-527,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis is a way to display the time complexity of a particular program in the form of equation by analyzing the code of that program.,1.0
-528,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis refers to the analysis approximated in the order of the fastest changing(growing) term without finding the exact duration for which it runs on the given problem.\n,1.0
-529,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is the analysis in order of the fastest changing, term without finding the exact term.",1.5
-530,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Analysis of an algorithm for the lowest and upper bounds as well as the average bounds of time and space that the algorithm might consume to devise and compare for the most efficient ones is known as asymptotic analysis of an algorithm.,2.0
-531,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",writing a time complexity of an algorithm and then going by it is what ,1.0
-532,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic Analysis is analyzing an algorithm and designating appropriate symbols with its time complexity.,1.5
-533,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The time taken by any algorithm for the completion of its full step is known as asymptotic analysis of an algorithm.,1.0
-534,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm analyzes the algorithm based on notations like theta notation to calculate the best-case scenario of a particular algorithm, big oh notation to calculate the worst-case time complexity and omega for average case. These notations compare the algorithms using graphs of functions used in algorithms.\n\nAsymptotic analysis involves calculating time complexities and space complexities.",1.5
-535,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is based a technique to analyze the Time and space complexity using Graph.\nits basically shows how with time the data grows, whether linearly or exponentially.",1.0
-536,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm is to execute the program in O(n) time complexity.,1.0
-537,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis of an algorithm is representing the time complexities of an algorithm in terms of O, theta and o.",1.0
-538,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm represents the time complexities of an algorithm in the form of O, o and omega.",1.0
-539,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis is used to analysis algorithms to give the time complexity and space complexity of the algorithms using certain notations. These are the standard notations developed so that the complexity analyzed throughout remains same. ,1.0
-540,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","there are the representations to provide the time and space complexity of an algorithm. eg big Oh' (O) , theta, omega.",1.5
-541,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is a method used in computer science to analyze the performance of an algorithm in terms of the input size. It helps to determine how the algorithm's runtime and memory usage grow as the input size increases, and to compare different algorithms in terms of their efficiency.",1.0
-542,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",the mathematical definition of an algorithm is its asymptotic analysis.,1.0
-543,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is calculating time and space complexity and representing them using different notations such as big O, omega and theta , i.e, best , average and worst complexity.",1.0
-544,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","The asymptotic analysis in done by the concept of best case , worst case and average cases which may occur in a program.",2.0
-545,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-546,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis is the analysis of an algorithm for different test cases - the best case, average case and the worst test case.\n It allows you to find upper and lower bound complexities for your algorithm. ",2.5
-547,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of algorithm is used to determine the time complexity of programs,1.0
-548,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm refers to the finding of best, worst and average time complexity of any algorithm.",2.5
-549,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",analysis of time taken,1.5
-550,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis the big O notation and the time complexity of an algorithm so that we can choose the most efficient algo,1.0
-551,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",omega and big O notation,1.0
-552,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm is the mathematical representation of the run time of a program.,1.0
-553,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","When we have made a equation to represent the running time of an algorithm and we use it to find out the best , average or worst case running time that could be defined as the asympotic analysis of an algorithm.",1.0
-554,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptomatic analysis of algorithm is is computing the running time of an algorithm in terms of mathematical function eg. f(n),1.0
-555,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","The asymptotic analysis of an algorithm is to analysis it on the basis of its best, average or worst case time complexity.",2.5
-556,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asympotic notations have the bounded the mathematical formula in the algorithm .And with the help of the asympotic notation we can define the complexity of the alghorithm .\n ,0.0
-557,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",it is the method of analysis in which we meeasure the nest worst and average case scenario of a algorithm .in asymptotic algorithm input is needed if there is no input thrn program will run in constant time,2.5
-558,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis goes through every line of the algorithm to find the mathematical equation of the time complexity of an algorithm. We can find either the best case, worst case or average time complexity of an algorithm using asymptotic analysis.",2.5
-559,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-560,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",time taken,1.0
-561,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","It is the analysis of the algorithm on three scenarios : best case , average case and worst case.\nOn the basis of the performance of the algorithm on these cases , we select the suitable algorithm for the given problem.",2.5
-562,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm is the the various calculations that take place to calculate the time which a particular code takes from run-time to its final compilation.,1.5
-563,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Process of calculating running time of an algorithm.,2.0
-564,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm."," Asymptotic analysis of an algorithm is to analize the algo on the basis of three major cases that are:\n Best case, Average case, Worst case.",2.5
-565,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm means analyzing an algorithm so that it takes as minimum time as possible and produces the best result.,1.0
-566,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","the asymptotic analysis of the algorithm is finding the trend of  time complexity of the algorithm when the number of elements (n) varies in the algorithm discarding other factors\nit can be exponential, log(N),o(N) and other ",1.0
-567,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis is the process of calculating the running time of an algorithm or its run-time performance. The goal is to determine the best case, worst case and average case time required to execute a given task.",1.0
-568,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",it is the analysis of how much time an algorithm or line of code will take to execute. it can be calculated for the worst case scenario or average scenario.,2.0
-569,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",uper and lower bound is done in this,2.0
-570,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm provides the necessary means to compare multiple algorithms and to find the best among them.\n,1.0
-571,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","exact time analysis,worst case time complexity and best case time complexity is the asymptotic analysis of the algorithm",1.0
-572,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm refers to defining the framing of its run-time performance.,1.0
-573,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is finding the best average and worst time complexities that a algorithm would take,2.0
-574,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.", Asymptotic analysis of an algorithm is done to find the complexity of the program such as time and space complexity so that we minimize the complexity if it takes more than required and make it an efficient and optimal algorithm,1.0
-575,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",analysis of algorithm based on it's graph function ,1.0
-576,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-577,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm is mathematical foundation of its run time performance.,1.0
-578,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-579,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm helps us to calculate maximum and minimum time complexity of given function.  ,2.0
-580,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Analysis of the the time and space complexities of an algorithm,1.0
-581,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis are the criteria of analysis is done there are worst case , best case , average case analysis.",2.5
-582,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.", explicit conplexities of algorithm.,1.0
-583,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-584,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis tells us the degree of an algorithm.\nFor example: bubble sort has the time complexity O(N^2).\nHere O( ) is the asymptotic notation which tells the time complexity of the algorithm.,1.0
-585,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",it basically tells about the running time of an algorithm using the mathematial computation like ,1.0
-586,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm means studying the algorithm on the basis of its upper bound or lower bound conditions that is analysis on the basis of best case , worst case and average case time complexities.",2.5
-587,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",In asymptotic analysis of an algorithm we find upper bound and lower bound ,2.0
-588,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptomatic analysis means when we analyze the algorithm on the basis if the most dominating term and do not consider the terms which don't have that much impact on the value of the time and space complexity,1.0
-589,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis of an algorithm refers to in how much time our program runs and how much space it requires to run to keep that in mind to design a best program.\nlike BIG OH (O(n)) here BIG OH refers to upper bound it will be maximum this value and not be greater than this.\nnext can be OMEGA (N) it refers to lower bound means min value, it will be greater than this value for sure.\nTHETA (N) refers to exact complexity of that particular algorithm ",1.0
-590,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",One of the method to analysis algorithm\nAsymptotic Analysis of algorithm is mainly actual time analysis of algorithm\nFrom which we get information about time measurement of algorithm,2.0
-591,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic notations are basically a measure of time that a code takes to compile and run . \n,0.5
-592,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The asymptotic analysis of an algorithm is the process of calculating the running time of an algorithm in mathematical units to find program limitations or run-tme performance.,1.0
-593,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","the most time can an algorithm take ,what is its upper bound,  least time it can take or its lower bound value and its worst case.\nafter this we have knowledge about each and every aspect of an algorithm.",2.5
-594,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","it define the mathematical foundation of its run-time performance.  by using this we can conclude the best case, average case, and worst case scenario of an algorithm.",1.0
-595,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",We try to write equation of time complexity using asymptotic analysis. For example -> T(n)= 3*O(n^2)+2*O(n),1.0
-596,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis uses mathematical operations to find the best case, worst case and average case time complexity of an algorithm. It uses the plot of the assumed and approximate values and then perform operation to find complexities.",1.0
-597,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis is analyzing an algorithm for its best, worst and average case and finding the time and space complexity for respective cases.",1.0
-598,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic notation are used to compare and find the space and time complexities of the algo and optimize it,1.0
-599,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm refers to analysis of time where time tends to infinity, covering all possible cases.",1.0
-600,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is used for the efficiency of algorithms when  there are very large inputs also or inpits are tending to infinity.,2.0
-601,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Comparison of time complexity based on the three notations.,2.0
-602,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Analysis of the algorithm in the best, worst and average case is called asymptotic analysis.",2.0
-603,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Helps to find the higher time limit of the algorithm( worst case), lower limit (best case) , average time (average case). It is done with help of asymptotic notations. Helps in comparison bw two algos",1.5
-604,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","In these type of notation the algorithm is judged on the basis of best case,worst case or average case scenarios.",1.5
-605,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Defining a mathematical statement in order to compute the time taken by the algorithm in best case, worst case ,average case.",1.5
-606,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-607,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-608,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis is the process of calculating the running time of an algorithm in mathematical units to find the program’s limitations, or run-time performance. The goal is to determine the best case, worst case and average case time required to execute a given task.",2.0
-609,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-610,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is used with the help of regression,2.0
-611,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",in which we compare time complexties of algorotihms on notations line big O\nomega etc.,1.0
-612,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis is to anlayze an algorithm on the basis of notations like Big Oh, Big Omega and Big Theta.",2.5
-613,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","theta notation, bigO notation, o notation, omega notation",2.0
-614,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The asymptotic analysis is the process of calculating the run time of a program in mathematical units to find the program's limitations or run-time performance.,2.0
-615,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-616,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis is a process in which time complexity and space complexity of the algorithms are represented in the form of asymptotic notations like big-o and small-o in the order of n, log(n), n*log(n) and many more.",1.0
-617,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",analysis of algorithm on the basis of time complexity is called asymptotic analysis,1.0
-618,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-619,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis refers to a criteria to determine the efficiency or complexity of the algorithms by the use of some symbols called \,1.0
-620,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-621,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-622,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","yes, algorithm  is asymptotic analysis   ",2.0
-623,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",the analysis in which we calculate the running time ,2.0
-624,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis is when we take different testcases for an algorithm and try to found out its best, worst and mainly the average case of the approach using various methods like Master Theorem.",2.0
-625,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","In asymptotic analysis, We use Mathematical tools and calculation to analyze the time and space complexity of the algorithm. ",2.0
-626,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm are the analysis of space complexity and time complexity of the algorithms,1.0
-627,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-628,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm is analysis of algorithm as a function of time like O(n) this tells the order of function in which the analysis of algorithm takes place but it does not tell the exact time the algorithm's analysis has taken ,1.5
-629,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","The asymptotic analysis of algorithm helps in defining the best, average and worst time and space complexity of the program.",1.0
-630,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",in asymptotic analysis we analyse the best average an worst cases for a particular problem solution and compare them in their complexities.,1.0
-631,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis is finding the value of asymptotic notations like BIGO(n). o(n), thetha(n), each notation represent different thing .",2.0
-632,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is the process in which we approximate or try to calculate the max amount of a time a process can take. For example the some of the major problems require time complexities like O(n), O(n^2), etc these mean that in the case of n inputs that maximum time that the process can take is of n^2 complexity. Asymptotes are lines that come very close to one another but never touch each other. Similarly in asymptotic analysis the process can come very close to the complexity we have calculate but it never touches or exceeds it. ",2.0
-633,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Defining the mathematical foundation of its run-time performance.,2.0
-634,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The asymptotic analysis of an algorithm is that how much time that particular algorithm will take considering an average time criteria whether it should be less or more.,1.5
-635,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is about finding the range of time and space complexity,2.0
-636,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic notations stand for notations used to define or to structure their complexities. There are many types of asymptotic notations that are big-O, Big-Omega, Small o ,small Omega. Analysis by which we define and find the complexities of the algorithm we have structured is called asymptotic analysis.",2.0
-637,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis refer to the time limitations while solving a particular problem that is when the time tends to infinitely large number what is the behaviour of the graph of the function.,2.0
-638,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is getting the worst, average and best time complexity and space complexity of a problem. ",1.5
-639,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic means tends to infinity. By asymptotic analysis we mean to find how our algorithm will work when it is given large inputs which are tending to infinity.,1.5
-640,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is analysing the algorithm according to its max and min time it takes.,2.0
-641,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is the analysis of a code done before the code runs.,2.0
-642,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is the analysis of Time and space complexities as per tight upper boud, tight lower bound and tight average boud according to the requirement.",2.0
-643,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",a asymptotic analysis of a algorithm is to analyze an algorithm based on loops its takes in time complexity or new space used in space complexity,2.0
-644,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm refers to the time analysis of a program based on its worst, average, and best case .",2.0
-645,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",it is a method to calculate time complexity of any algorithm.,2.0
-646,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm has an ,2.0
-647,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",It is the mathematical approach of finding time or space complexity of an algorithm in which an algorithm is defined as a function of n (f(n)). ,2.0
-648,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-649,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of algorithm is describe the mathematical framing while it is running,1.0
-650,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","The asymptotic analysis of an algorithm includes see how the function is growing over time using mathematical functions. we are more interested in degree of the polynomials. We don't need to look at all the cases but get a brief overview. Like we can look at the best case, worst case and average case and see overall and get idea of how the algorithm would be performing. these analysis then help us compare the different algorithms. ",1.5
-651,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptomatic analysis of an algorithm is when algo is analyzed on the basis of input size.,1.5
-652,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","in asymptotic analysis we take different test cases and find the best, worst and average test  case",1.5
-653,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",It is the process of calculating the run time of an algorithm.\n,2.0
-654,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","The asymptotic analysis of an algorithm is done to find out the Best, Average or worst case for the Time or Space complexity of a program",1.5
-655,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of the algorithm is to calculate or estimate the running time of the algorithm and therefore calculating the worst best and average case of the particular algorithm,1.5
-656,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-657,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic Analysis of an algorithm is ,1.5
-658,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm is ,1.5
-659,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-660,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The process of calculate the running time of algorithms in mathematical units to find the program limitations.,1.0
-661,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",It refers to giving mathematical foundation of its runtime performance.,1.0
-662,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",the analysis of the upper and lower bound of an algorithm is called its asymptotic analysis,1.5
-663,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",The asymptotic analysis refers to the study of running time of an algorithm.,1.5
-664,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Analysis based on algorithm's time complextiy is called asmptotic analysis,2.0
-665,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic ananlysis is the study of upper bound time or lower bound time complexity analysis of a particular algorithm.,2.0
-666,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis is the process in which we  calculating the run time of an algorithm in mathematical units to find the program’s limitations and run time perfomance.,2.0
-667,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",refers to computing the running time of any algorithm in mathematical units of computation.,1.0
-668,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis include finding worst case time , best case time and average case time complexity for a particular algorithm.",2.0
-669,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-670,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",approx time complexity analysis of an algorithm.,1.0
-671,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm refers to defining the mathematical foundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case, and worst-case scenario of an algorithm.\n",1.0
-672,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","it is the analysis in which we analyze the running time complexity of an algorithm for best ,average and worst case.",1.0
-673,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",An asymptotic analysis  of an algorithm is to analyze the code time complexity at every operation of the proposed algorithm so that we can get \nthe information about the time complexity of code\n,2.0
-674,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis of algorithm is analysis of algorithm in best time case ,average time case, and worst time case",2.0
-675,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm."," increases the time complexity, any \",1.0
-676,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm refers to defining the mathematical foundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case, and worst case scenario of an algorithm.",2.0
-677,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","In which we compare the problem statement at hand with a pre-existing analyzed statement. And we just try to reduce the complexities in the form of the other one. In this manner we can comment on the best case, worst case and average case time complexities.",1.0
-678,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-679,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis represent to analyzing respective algorithm on basis of time and space:\nIt includes finding upper bound, lower bound ,average bound of a system by studying f(n),g(n) and respective best case, worst case and average case.  ",1.0
-680,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm is the ananlysis of algorithm to figure out the complexity of the algorithm.,1.0
-681,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis is done to check the time complexity of a program so that we can get an idea about the best and worst time it is taking to run a particular program.,1.0
-682,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptomatic analysis gives a rough idea of the time the algorithm will take in execution given we have the variables like user-defined inputs.\nIt can be used for the analysis of the time complexity, space complexity or any required auxiliary space.",1.0
-683,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis is done to check how good the Algorithm is and how much time it would be taking it is mainly used in calculating time complexity of a algorithm . there are different types of asymptotic notations like big O, theta, omega,",1.0
-684,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","it is the analysis of time complexity of a given algorithm to get the complexity in best, average and worst time cases",1.0
-685,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","It is the best , average and worst case time complexity of an algorithm. These can be used for comparison. ",1.0
-686,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-687,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",It is analysis of the algorithm using a criteria in which we find time complexity of an algithm.,1.0
-688,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis of an algorithm is a process to determine the time complexity of an algorithm using various theorems ,laws etc. , like master theorem can be used to find it's complexity effectively.",2.0
-689,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",it  helps in giving the time complexity of a problem,1.5
-690,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",It is the analysis of time complexty and space occupied.,2.0
-691,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","thats the time depending approch for algorith.means that how much time will take the algorithm running ,like its have notation for describe the time complexity.  ",2.0
-692,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",the analysis of the running time of a particular algorithm is called asymptotic analysis. it is algebraic or graphical in nature.,2.0
-693,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of algorithm is an analysis where we calculate the time complexity of the algorithm.,2.0
-694,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",It refers to the analysis of time and space complexities of an algorithm,2.0
-695,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Determination of how an algorithm in perform in its best case and its worst case scenario helping .Time complexities are denoted with the symbols accordingly,2.5
-696,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic Analysis of an algorithm 1)best case2)average case 3)worst case ,2.5
-697,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-698,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis deals with best case worst case and average case of an algorithm,2.5
-699,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","The asymptotic notation of an algorithm is the representation of time and space complexity of the algorithm in terms of  Big O, Big omega notations, By this we can judge two algorithms whether the given algorithm should preffered over other or not.",2.5
-700,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",this the analysis of space and time complexity using asymptotic notations like \nbig O\nbig alpha\nbig omega,2.5
-701,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis is the analysis of algorithm based on two complexities time and space. in asymptotic analysis we use asymptotic notation to give best case, worst case and average case  out comes of the algorithm.",2.5
-702,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic Analysis of an algorithm refers to the analyss,0.5
-703,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Considering the efficiency of algo in three cases \nThe Best Case\nThe Worst Case\nThe Middle Case ,2.5
-704,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",\nBig theta : average case\n\nBigO: best case ,0.5
-705,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of algorithm refers to analysing time and space complexities using various notations to fit various  requirements like having time or space complexity less than, greater than or equal to a certain order of n.\n",2.5
-706,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis is obtaining the time complexity using mathematical expressions  ,2.5
-707,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-708,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis means to find the various and more efficient approach to a problem using the different asymptotic notations ,0.0
-709,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-710,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-711,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","big omega, big theta, big delta",0.0
-712,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-713,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis of an algorithm is the measurements of time taken by the algorithms in asymptotic denotions(namely, oh, omega and theta). In those too, it is divided into different parts, such as big oh, big omega, small oh, small omega.",2.0
-714,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptomatic notations can be analysed in 3 types \n1) big o notation - it is the upper bound\n2) omega notation - it is the lower bound\n3) theta notation -,2.5
-715,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",there are three type of asymptotic analysis of an algorithm:\n--> Big O notation.\n--> Omega notation.\n-->Theta notation.,2.0
-716,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm  basically a notations in which a  algorithm is performed,0.0
-717,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm reffers to the practice of analysis on the basis of notation in which complexity can be expressed,2.0
-718,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",In asymptotic analysis of an algorithm we categorize the algorithm in 3 categories so that we can figure out what max time our algo will take in worst case and min time in best and avg. time in avg. case. With the help of analyzation we can find Time Complexity of the Algorithm.,2.5
-719,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis of an algorithm is the analysis of the time complexity the algorithm takes, where big omega is for best case big thetha for average case and big O for worst case time complexity.  We analyse through these aasymptotic algorithm so we can analyse our code for time complexity.",2.5
-720,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algorithm is to analyse the problem and divide thye problem into fewer subproblems.,0.0
-721,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Types of asymptotic analysis are :-\nBig O notation = upper bound graph\nOmega notation = mid bound graph\nTheta notation = lower bound graph,2.0
-722,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","Asymptotic analysis is the practice of figuring out the space and time complexity of the algorithms in order to determine which algorithm would be the best to use. A key point here is that we take the complexity of the functions into consideration when the independent variable is tending to infinity. Hence, the name asymptotic.",2.5
-723,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",Asymptotic analysis of an algorithm is to understand the space and time complexity of any algorithm. ,2.5
-724,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",in which we get the best average and worst case of algorithm and according to that we can apply the suitable technique.,2.0
-725,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",,0.0
-726,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis refers to using substitution method or master theorem to find out the mathematical complexity of our algorithm,0.0
-727,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",asymptotic analysis of an algo is is we measure a performance of algo in the term of different mathematical notation like big o and many more ,2.5
-728,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm."," asymptotic analysis is the analysis of the code in terms of time complexity of the code.\nsome asymptotic notations - O(worst case), Theta(average case) and omega(best case)",2.5
-729,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",min time omega\nmax time  big oh\navg time  theta\nsame as storage,2.0
-730,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",they can never change the time.,0.0
-731,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.",the analysis which uses asymptotic notations for telling the time complexity of given program or problem ,2.0
-732,What is asymptotic analysis of an algorithm?,"Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.","asymptotic analysis are basically for the prefix , suffix problem in a given question.",0.0
-733,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-734,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"BigOh(theta), bigoh(omega), smalloh(theta)",2.0
-735,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,its calculate the running time of the algorithm,1.0
-736,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"There are three asymptotic notations: 1) O(): The big O notation shows the worst case complexities, 2) theta: The theta notations show the average case complexity, 3) omega: The omega notation shows the best case complexities",2.5
-737,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"In asymptotic notations we have Big oh notation which is the upper bound, Theta notation which is average, Omega which is the lower bound.",2.5
-738,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notations are bigoh(n),average o(n) and omega(n).",2.5
-739,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations is used  to describe the running time of an algorithm .how much time algorithm takes with a given input 'n'. there are three notations. \nbig O \nbig theta\nbig omega.,2.0
-740,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Big oh , omega ,theta.",2.0
-741,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,-> Big oh - worst case time - the algorithm will not take more than this time\n-> theta - average case time - the algorithm will take average this much time\n-> omega - best case - the algorithm will take minimum this much time,2.5
-742,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notation is a mathematical notation used to analyze the time complexity and runtime of an algorithm for a large input,2.5
-743,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,To study the time complexity and space complexity of a given problem we use notations that are known as asymptotic notations.,2.0
-744,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Notation used to represent the time , whether it is linear , quadratic , cubic , and etc. .\nsome of the notations are :\n0(1) --> constant\no(n) --> linear",1.0
-745,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are which declares the meaning like ending the code, comparing the values, using brackets etc.",1.0
-746,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notations include Big O notation, theta notation and omega notation. These are used to denote the upper bound, lower bound and average time taken by any algorithm to function. eg O(nlogn), o(n^2)",2.5
-747,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations are :\nbig o \ntheta\nomega,2.0
-748,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,We have various asymptotic symbol :-\n- Big O is for determining upper bound.\n- Omega is for determining lower bound.\n- Theta if for determining exact value.,2.0
-749,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,1. best case analysis --> sigma Notation\n2. average case --> theta Notation\n3.worst case --> Big Oh Notation,2.5
-750,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-751,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"they are omega, theta, big O.",2.5
-752,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,examples are:\n1)Big O : f(n)<=g(n)\n2)Big Omega : f(n)>=g(n)\n3)Little o : f(n)<g(n)\n4)Little omega : f(n)>g(n),2.5
-753,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations gives us the information regarding the time and space complexities in various cases like the best , worse and the average cases. It helps us in comparing the efficiency of the program from various angles. It has O() for worst case , omega() for best cases and theta() for average cases.",2.0
-754,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notation gives us the information about the space and time complexities in various cases, it could be the best, worst and average case. ",2.0
-755,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations like big O,omega and theta which represents the possible cases an algorithm can be solved. Big O represents best case scenario ,theta represents average case scenario while omega gives worst case scenario of an algorithm. It is identified by the functions used in algorithm which makes it easier to differentiate and to justifies particular scenario, for space and time complexities.",2.5
-756,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,There are three asymptotic notations which are used for time complexity. These notations are:\ni) theta ( )\nii) O( ) \niii) omega ( ),2.0
-757,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,to check lower bound of the solution. we use asymptotic notations. ,1.0
-758,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big O (O) -worst time complexity\nTheta- Average time complexity\nOmega- Best time complexity,2.5
-759,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The Notations that are used to denote the functions generated by the algorithms are knows as asymptotic notations. For example, Big Oh, Theta, Omega are the bounds of an algorithm, Big Oh denotes the upper bound, Omega denotes the lower bound and Theta denotes the average.",2.5
-760,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The notations generated to denote a function generated for the algorithms are called asymptotic notations. Omega, theta and Big oh are the bounds for an algorithm and denote lower, average and upper bound respectively.",2.5
-761,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-762,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations mean representing the time complexity of an algorithm in the form of an equation (recurrence relation).,2.5
-763,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big O(n),1.0
-764,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,The asymptotic notation is to represent the given problem in terms of time complexity. so that if there is chance to reduce its complexity then we can do it by applying suitable complexity. \n1)Big O notation\n2)theta notation\n3),2.5
-765,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations are mathematical terms which are used to analyze an algorithm. They are basically used to measure the time taken by an algorithm.\nsome common examples of asymptotic notations are :-\nBig-oh,\nBig- omega\ntheta ,etc. ",2.5
-766,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-767,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic Notations are basically a method to represent the given Algorithm in Mathematical terms . Some specific notations are being defined to check or state whether the time complexity of an algorithm will be in which of the form of asymptotic notations.\nFoe ex; 1. Big-oh\n             2. omega \n             3. theta ,2.0
-768,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations tells about the running time of a program. It tells us how much time can a program take to give an output. Asymptotic notations are divided in big O, theta and omega notations.",2.0
-769,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic analysis are big O, theta and omega.",2.0
-770,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"These are a kind of approximations of time and space complexities which tells us the program that in how much time the algorithm is taking to run the program. These are denoted by some kind of symbols such as theta, Small and Big O. ",2.0
-771,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are the ways to represent the time complexity of a program. Example: bigO, omega(w), theta.",2.0
-772,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big O\ntheta\nomega\ndelta\netc.\n,2.0
-773,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are the notations used to express complexities, for example:\nBig O\nBig Theta\nBig Omega\nSmall O",2.0
-774,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Analysis of an Algorithm can be done using notations that give us the information about the lower and upper bounds or exact constant time or space that the algorithm might take:\n1) big O notation - It specifies the  minimum upper bounds of time and space that the algorithm might take.\n2) omega notation - It specifies the highest lower bound of time and space that an algorithm might take.\n3) theta notation - It specifies the average time and space that an algorithm might take.,2.5
-775,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"it is a method for the  notation of the time complexity of an algorithm. Big oh, theta, small o",2.5
-776,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big o - O : Worst Case Time Complexity \ntheta- Average Case Time Complexity\nomega- Best case Time Complexity,2.5
-777,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,There are different types of asymptotic notations:\n(i)Big-O\n(ii)Omega\n(iii)Theta\n(iv)Small-o,2.5
-778,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,There are various asymptotic notations like:\n1. Big oh (O) Notation: Calculates the upper bound time complexity. Shows the worst-case input scenario for a particular algorithm.\n2. Theta Notation: Calculates the lower bound time complexity. Shows the best-case input scenario for a particular algorithm.\n3. Omega Notation: Calculates the average case time complexity. Shows the median-case input scenario for a particular algorithm.,2.5
-779,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,the asymptotic analysis use certain notation in which the time complexity of an algorithm can be written.\nthose notations are called as asymptotic notation.\nthere are major 3 notations:\n1)Big O\n2)Big omega\n3)Big Theta\n,2.5
-780,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic Notations are:-\nBig of O\nBig of Theta\nBig of Omega,2.5
-781,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-782,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are in the form of Big O(O), small O(o) and omega.",2.0
-783,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are the representation in which the space and time complexities are analyzed and shown. Few asymptotic notations are Big O Notation, Theta Notation etc.",2.0
-784,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"there are the representations to provide the time and space complexity of an algorithm. eg big Oh' (O) , theta, omega.",2.0
-785,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are a way to express the time complexity of an algorithm in computer science. They provide a formal way to describe the rate of growth of the running time of an algorithm as the size of the input increases. The three main asymptotic notations are Big O, Omega, and Theta, which are used to describe the upper, lower, and tight bounds of the time complexity, respectively.",2.0
-786,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"big O notation, the big theta notation and the big omega notation",2.0
-787,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"different notations such as big O, omega and theta , that represent best , average and worst complexity",2.0
-788,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"There are big o , theta and omega.",2.0
-789,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-790,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are the notations to represent different test cases - the best case, the average case and the worst case. \nThese notations are theta, big-O notation and the omega notation. The theta is represented for average case, big-O notation for worst cases and the omega for the best test case.",2.5
-791,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations include Big Oh, Theta, and Omega.\nBig Oh is for the worst case time complexity\nTheta is for the closest time complexity\nOmega is for the least time complexity",2.5
-792,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,1)Big O\n2)Omega,2.0
-793,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"notations used to measure time like big o , theta , omega ",2.0
-794,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,It is used to describe the running time of an algorithm,1.0
-795,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Its Mathematical notation to compare between two things which is bigger or lesser ,1.0
-796,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations are: \n1.best case ( omega notation)\n2. average case (theta notation)\n3. worst case (big O notation),2.5
-797,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,The equations which we made as per the code for finding the running time of a algorithm are known as asympotic notations.,1.0
-798,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptomatic notation means the amount of time the algorithm will take to run like eg. O(n) or Theta time.,1.5
-799,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are theta, ohm and O(big O) FOR best, worst and average case respectively.",2.5
-800,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,It is a mathematical notations that represent the complexity or performance of an algorithm for large inputs or when the input size changes.,2.5
-801,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,it is used to describe the run time of a given algorithm it  gives how much time does a algorithm take to run,1.5
-802,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are: \n1. Omega\n2. Theta\n3. Big - O,0.0
-803,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-804,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation., ,0.0
-805,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"It is used to show the complexity of the algorithm in best , average or worst case.\nThere are three asymptotic notations :\n1. Big O notation \n2. Theta notation \n3. Omega notation ",2.5
-806,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are the mathematical notations used to describe the running time of an algorithm when the input tends towards a particular value or a limiting value.,1.0
-807,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Mathematical notation used to describe running time of an algorithm.,1.0
-808,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are the notations which is widely used in order to identify the three cases of time complexity analysis of an algorithm, that are\n->Best case (thetha)\n->Average case(Omega )\n->Worst case(Big O) ",2.5
-809,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notations are o, big o, and delta. These are notations used to denote the best, average and worst case of an algorithm.",2.0
-810,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are \n1. BIG O (O) worst time complexity\n2. theta(N) Best time complexity \n3. average case time complexity\n,2.5
-811,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are mathematical notations that are used to analyze the runtime of a given algorithm for a large input. It helps us to compare the runtimes of different algorithms without actually calculating their runtimes manually. Asymptotic notations are used only for larger inputs. ,1.0
-812,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"big O, small o, big omega, small omega",2.0
-813,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big o notaion ,1.5
-814,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"There are symbols depicting the time complexity (Big O, Theta, Omega)",2.0
-815,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"o(n),O(n),T(n),W(n)",2.5
-816,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic Notations are the mathematical notations used to describe the running time of an algorithm when the input tends towards a particular value.,2.0
-817,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big O\ntheta \nomega \ntime complexities,2.0
-818,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are denoted using O ,THETA AND OMEGA.Big o and small o has f(n)<g(n),big w and small w has  f(n)>g(n) and theta has c1n<fn<c2n",2.5
-819,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,there are the major notations -\nbig o\nbig theta\nomega\ntheta\no,2.5
-820,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Big Omega, Small Omega, Big O, Small O, Thetha",2.0
-821,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are theta , omega, big o . These are used to show the time complexity of a given code. ",2.0
-822,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-823,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are big O , theta and omega notations . ",2.0
-824,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big O\nTheta ,2.0
-825,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"These are the notations that denotes the different cases that the algorithm has analyzed Big O denotes worst case , Big Omega denotes average case , theta denotes best case.",2.5
-826,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"big O, theta",1.0
-827,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"there are certain rules in which we make coefficients of lowest power of x equal to the highest power and then find n0,c0 and f(n)",1.0
-828,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Following are the asymptotic notations:-\n1. O( ): Tells about the worst case complexity\n2. o ( )\n3. Theta ( ): Tells about the average case complexity\n4. Big Omega ( ): Tells about the best case complexity\n5. Small Omega ( ),1.0
-829,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,it basically tells us about the running time of an algorithm,1.0
-830,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,The asymptotic notations are:\n1- Big Oh()\n2- Big Omega()\n3- Big OTheta,2.0
-831,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Big O , Big omega , theta , small omega , small O",2.0
-832,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptomatic notations are expressed to denote the different cases of the run time of algorithms. Such as big o for the worst case, theta for the average and omega  for the best case scenario of an algorithm.",2.0
-833,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"like BIG OH (O(n)) here BIG OH refers to upper bound it will be maximum this value and not be greater than this.\nnext can be OMEGA (N) it refers to lower bound means min value, it will be greater than this value for sure.\nTHETA (N) refers to exact complexity of that particular algorithm ",2.0
-834,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Notation to represent time/Space Complexity of algorithm.\ntheta(N)\nFrom which we get information about time measurement of algorithm\nwhich is essential to know about whether a algorithm is efficient or not,2.0
-835,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"they are of three types , big o , omega and theta. ",2.0
-836,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are the notations which are used to describe the running time of an algorithm. 3 notations are big O,big thetha and bigf omega.",1.0
-837,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations are of three types big O, big omega ,big theta.\nasymptotic notations tell us about the upper bound, lower bound and worst cases of the algorithm.",1.0
-838,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-839,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Omega(n)\nTheta(n)\nOhm(n)\nThey are three notations.,2.0
-840,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are used to time the best case, worst case and average case time complexity of a function by using mathematical calculations and approximation comparisions.",2.0
-841,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations are notations used to represent time and space complexity of an algorithm for best, worst and average case. Ex- big O, theta and omega.",2.0
-842,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big O Notation\nTheta Notation\nLittle o notation\nOmega Notation,2.0
-843,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are Big O,  theta and  omega . These denote the worst case, average case and best case time complexities. They are decided according to the upper bound , and lower bounds of the Time taken in different cases.  ",2.0
-844,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big O notation\nlittle o notation\ntheeta notation \nomega notation,2.0
-845,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,O(n)\nOmega(n)\no(n),2.0
-846,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,O() for worst case\ntheta() for average case,2.0
-847,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Mathematical symbols used for asymptotic analsysis like big O, big omega,etc., ",2.0
-848,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The aymptotic notation denote that given algo is best case,worst case or average case .",2.0
-849,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,They are mathematical notations used to describe the running time of an algorithm  ,2.0
-850,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic Notation is used to calculate the running time of an algorithm , how much time an algorithm will take  \",2.0
-851,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,The asymptotic notations are as follows:\n- Big O \n- Small o\n- Theta Notation,2.0
-852,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are mathematical notations that are used to analyze the runtime of a given algorithm for a large input. It helps us to compare the runtimes of different algorithms without actually calculating their runtimes manually.,2.0
-853,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-854,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations are notations that ,1.0
-855,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,1. Big O\n2. Big Omega\n3. o(n),1.5
-856,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are a basis to examine the time complexity of an algorithm.,1.0
-857,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"it is a way of finding a comparison point, by which we can compare two algorithm which are used to do the same piece of work but who does more efficiently.",2.0
-858,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notation is a mathematical notation used to define the run time of an algorithm as the input size reaches infinity. It helps to compare the runtimes of different algorithms without actually calculating their runtimes manually.,1.0
-859,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-860,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,1. big-o notation\n2. small-o notation\n3. theta notation,1.0
-861,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Notation like omega , big-O ,little -O etc are asymptotic notation",1.0
-862,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big and small O.,1.0
-863,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,.,0.0
-864,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-865,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-866,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-867,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"omega ,gamma, theta are the asymptotic notations used for time complexity omega is for average case of a program similarly theta is for worst case of a program and gamma is for best case of a program ",2.5
-868,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notations are omega(time), theta(time), ohm(time).",2.5
-869,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notation are the mathematical tools which helps to find the time and space complexity of an algorithm,2.5
-870,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are defined as\nBig O notation\nBig omega notations\nBig theta notations,2.5
-871,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are big O, big omega and big theta. Big O means time taken by algorithm is less than it is written, Big theta means time taken is more than it is written.",2.5
-872,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations are used to denote the maximum time that an algorithm can take in its execution, minimum time that an algorithm can take in its execution and it can also denote that the time complexity of an algorithm lies between maximum time taken and minimum time taken. the asymptotic notations are O(n) omega(n) and theta(n)",2.5
-873,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The 3  asymptotic notations are big omega(maximum time and space required),theta(average time and space required),and the last one which describes the minimum time and space required for the program.",2.5
-874,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations are used for analysis of algorithms for best average and worst case according to their complexities. big O big theta and big omega ,2.5
-875,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,BIG O notation \n o notation \nthetha notation\nbig omega  ,2.5
-876,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notations are O(n), theta(n), and omega(n). They are  for upper bound, middle bound and lower bound respectively. ",2.5
-877,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Used to define running time of an algorithm,2.5
-878,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are  the notations which denote  the maximum or we can say that the worst case time scenario and the minimum or the best case time scenario which that particular algorithm will take while executing .,2.5
-879,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"the notations to describe the time complexity of algorithm like O(n), etc...",2.5
-880,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"These are notations used to define complexities of algorithm , Examples of which are Big O, Big Omega, Small O, Small Omega etc. These notations are used in order to represent complexity of the algorithm we have written and at par helping the programmer to write more efficient code.",2.5
-881,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big O\nBig omega \nBig theta,2.5
-882,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are big omega which describes the best case , then theta which describes the average case and the third one describes the worst case.",2.0
-883,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations includes big omega ,big theta, big oh. These are used to set upper and lower bound of how much time an algorithm will take.",2.0
-884,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"big O, small o, omega are the notations.",2.0
-885,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big O\nsmall o\ntheta\n\n,2.0
-886,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are the notations used to analyse the time and space complexity of different algorithms and hence it become very easy to compare any two algorithms.\nThere are three major asymptotic notations-\n1- Bit-O notation- In this notation we try to find out an upper tight bound on an algorithm time and space complexity.\n2-Big-Omega notation- In this notation, we try to find out an lower tight bound on an algorithm time and space complexity.\n3-Theta notation-This notation provides an average bound of an algorithm to it's time and space complexity.",2.0
-887,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,there are three main notations in asymptotic notations:\n1) big O\n2) big omega\n3) theta,2.0
-888,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notation for :\nWorst case- O(n)\nAverage case- Q(n) (theta of n)\nBest Case- Rho(n),2.0
-889,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"These notations tell us more about the time complexity in best, average and worst case. Example: big o, big omega, theta.",2.0
-890,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are used ,2.0
-891,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Function of n, used to find time and space complexity of an algorithm",2.0
-892,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-893,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation., asymptotic notations  are  used to represent time complexity of asymptotic .,2.0
-894,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notations are Omega, Big Oh, and Theta. Big Oh tells best case time, Theta gives us the average case and Omega gives worst time.  They tell us how in different cases the algorithm is performing. giving us time and space complexities in form of polynomial and their degrees. ",2.0
-895,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big O notation\nBig Omega notation\nBig Theta notation,1.0
-896,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notation are theta(time), omega (time), ohm(time).",2.0
-897,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,They are the mathematical notations used to describe the running time of an algorithm.,2.0
-898,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Theta Notation\nBigO(n) Notation\nOmega Notation,2.0
-899,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic Notations are Big O ,Theta, omega",2.0
-900,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-901,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-902,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-903,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are notations used for mathematical describe the run time of algorithms.,1.5
-904,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notations may be gamma , omega , theta ",1.5
-905,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations are used to tell the complexity of an algorithm or program,1.5
-906,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Big O, Big Omega and Big theta are the asymptotic notations",1.5
-907,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"there are three asymptotic noitaions in algorithm analysis namely Big O(O) that finds out worst case complexities, Theta notation that finds out average case complexities and omega notations which find out best case complexities.",1.5
-908,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Theta, Omega, Gamma",1.5
-909,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,There are 3 asymptotic notations .\n1)Big O notation\n2)Theta Notation \n3)omega notation,1.5
-910,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notation is a the way to describe the run time of an algorithm. big O notation, theta notation, omega notation are the basic examples.",1.5
-911,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,these are symbols used to describe the running time and complexity of any algorithm.\nthere are three notations:\n1. big O\n2. big theta\n3. big omega,1.0
-912,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are used in cases where algorithm behaves differently in different cases it includes theta notation.,1.0
-913,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-914,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"there are mainly three types of notations omega, theta and bigO.",2.0
-915,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are mathematical notations that are used to analyze the runtime of a given algorithm for a large input. It helps us to compare the runtimes of different algorithms without actually calculating their runtimes manually. \nThere are mainly three asymptotic notations:\nBig-O Notation (O)\nOmega Notation\nTheta Notation \n,2.0
-916,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"big o, omega ,theta",2.0
-917,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations\nare the notations to represent of time of complexity of any code \nexample:-\nBig O notation \nTheta  notation\nomega Notation,2.0
-918,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"big O(n), omega of(n),theta(n)",2.0
-919,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.," increases the time complexity of the same. Space complexity increases with say each 1d array taking O(n) time, 2d array taking O(n^2), etc.",2.0
-920,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic Notation is used to describe the running time of an algorithm and  how much time an algorithm takes with a given input, n.",2.0
-921,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,O( )\nOmega( )\n,2.0
-922,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic Notations are the symbols/notations to denote the time and space complexity of an algorithm.\nEg: Big-O (O) , Big-theta , Omega, etc.",2.0
-923,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are the special characters or symbols used to represent time /space taken by the specific algorithm.,2.0
-924,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are symbols or notations used to represent complexity of an algorithm.\nThese are omega,theta and big O wherein they represent the best case,average case and worst case complexities",2.0
-925,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"These are characters and symbols that are used to define the time complexity of the algorithm.\neg: BIG O, Omega etc.",2.0
-926,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptomatic Notations like Big Oh (represented by O), Omega and Theta are notations used to define various cases that the algorithm is run. Big Oh can be seen as the analogous to Worst Case, Omega can be seen as the analogous to Best Case and Theta is used for exact calculations or maybe in some cases - Average Case.\nOmega is the least used among the three. Apart from these, we also have small Oh etc. (but they are not taught in the syllabus much for the sake of avoiding confusion or maybe some uniformity/brevity)",2.0
-927,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations are notations which define the complexities of algorithm like big O notation , theta notation ,omega notation, where bigO gives upperbound ,omega gives best case.",2.0
-928,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptotic notations are symbols used to denote different time complexities like big o, theta and omega",2.0
-929,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymptomatic notations are symbols used to denote different time complexities like big O, theta and omega.",2.0
-930,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-931,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,The asymptotic notations is a method in which we find a function which is is greater than the time function of our algorithm and we say that function will take maximum time less than that.,2.0
-932,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"There are various notations like BIG O ,BIG THETA,BIG OMEGA describing the worst case, average case and best case time complexity respectively.",2.5
-933,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big O notation\nsigma notation\n(small) o notation\ntheta notation,2.0
-934,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"The asymptotic notation are-\nbig O. omega, theta",2.5
-935,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"asymtotic notations are big O ,big omega and big theta. Thats describe the upper bound ,lower bound and exactb time which nthe algorith will take .",2.5
-936,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"they describe the manner of time that is required to run a particular function. big O, big omega, big theta are asymptotic notations. ",2.5
-937,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,There are three asymptotic notations - \nO -> This one is used to show worst case scenario.\nTheta -> This is used to show the average case.\nOmega -> This is used to show best case.,2.5
-938,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"These are notations used to represent the time and space complexities of an algorithm, these are:\n1. big Omega\n2. small Omega\n3. big O\n4. small O\n5. big theta\n6. small theta",2.5
-939,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big O :-For checking the upper bound\nBig Omega :- For checking the lower bound\nBig Theta :- Fore the average case scenario,2.5
-940,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic Notations of an Algoithm basically defines the algorithm which tend towards infinity ,0.5
-941,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-942,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,BigO : Average case\nBigTheta : Best case\nBigOmega : Average case,2.5
-943,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notations are a way of representing the time complexity of a particular algorithm by representing the time complexity in terms of big o and big omega where big o notation is considered to be the best as it gives the average of the time complexity of the algorithm.  Generally we preffer to judge an algorithm in terms of its worst time complexity as by judging to algorithms by their best case will not give us the better result.,2.5
-944,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,these are the tools which are used to analyze a particular algorithm\nBig O\nbig omega\nbig alpha,2.5
-945,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"these are the symbols used to define the complexity of the algorithm in the best case , worst case and the average case, then there are loose bound and tight bound notationns\nthese are donated by symbols O, sigma and theta",2.5
-946,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-947,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"These notations take use of special symbols how efficient a program is by considering 3 things best case(Omega), middle case(Rho) and the worst case( Big(O)) ",2.5
-948,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-949,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Notations made to perform asymptotic analysis are asymptotic notations . They are:\n1. Big Oh: it is the tight upper bound.\n2.Big omega: tight lower bound.\n3. Theta: average case\n4.Little oh: loose upper bound.\n5. Little omega: loose lower bound.,2.5
-950,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notation  is when  time complexity of an algorithm is  expressed using theta and omega notation,2.5
-951,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,this is used to define the running time of algorithm,1.0
-952,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,various types of asymptotic notations are \n1) big O(n)-best case \n2) theta(n)-\n3) sigma (n)-worst case,2.5
-953,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notation are used to denote the time complexity of the program / algorithm:-\n1:- Big O \n2;- Big Omega\n3:- Big theta,2.5
-954,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-955,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notation   are the notation that are complex in nature,0.0
-956,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-957,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic Notations are the calculations of time complexity of an algorithms.\nSome of them are:-\nBig Oh: Denotes the maximum time taken by the algorithm!\nBig Omega: Denotes the minimum time taken by the algorithm!\nBig Theta: Is the average time between the two!\n,2.5
-958,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptomatic notations are notations through which we can give time complexity or space complexity of algorithm,0.5
-959,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-960,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-961,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"these are Big 0 , omega ie , low level and theta notation",2.0
-962,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,i) Big O Notation : Upper Bound of algorithm  c1g(n) > f(n)\nii) Omega Notation:  Lower Bound of algorithm  c2g(n) <= f(n)\niii) Theta Notation:  main function  c1g(n)> f(n) >= c2g(n),2.5
-963,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Big O, Big Omega, Big Thetha",2.5
-964,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-965,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-966,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"Asymptotic notations are mathematical notations to represent the complexity of the algorithm. The three most popular asymptotic notations are Big O, Big Omega and Big Theta notation. Other two notations are small omega and small o.",2.5
-967,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notations are those which is used to represent the time and space complexity as a function of an algorithm.,0.0
-968,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big theta\nomega\n,1.5
-969,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,,0.0
-970,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Big oh of n\nTheta of n\nOmega of n\netc,2.5
-971,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,Asymptotic notation is the mathematical term to measure the performance of the algorithm like optimization's in the term of time complexity and maintaining the system power and the space consumption of the server ,0.5
-972,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"some asymptotic notations - O(worst case), Theta(average case) and omega(best case)",2.5
-973,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big oh\nomega \ntheata,2.5
-974,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,big omega & big theta.,2.5
-975,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,"the notation such as big o ,omega and theta are asymtotic notations and used for telling the time complexity for the algorithms ",2.5
-976,What are asymptotic notations?,Asymptotic analysis can provide three levels of mathematical binding of execution time of an algorithm – (1) Best case is represented by Ω(n) notation. (2) Worst case is represented by Ο(n) notation. (3) Average case is represented by Θ(n) notation.,asymptotic notation are the notation which is  ,0.0
-977,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-978,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure are those which store data in a linear form and not in the form of levels examples include array, stack,queue,linked list",2.5
-979,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure ,data structure where elements are arranged linearly or sequencially like array ,queue",2.5
-980,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear Data Structure are the data structures that store data in the memory contiguously, i.e, data is stored one after another",2.5
-981,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure is a data structure that is represented in memory contiguously. For example, array is a linear data structure. Each element is represented in memory one after other. If one element is at 4000 then next element of array will be at 4001(in case of char array).\n\nNon linear data structure would be a linked list where different nodes are present at different location in memory and each one is linked using pointers to their memory addresses.",2.5
-982,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are data structures used to store data in a 1-dimensional array. \nTraversing the total data structure will only take take O(n) time complexity.\nExamples : array,string",2.5
-983,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure., linear data structure is one where data items are arranged in a linear fashion.  each member is attached to its neighboring element. the structure allows single level data storage because the data elements are stored in linear fashion. ,2.0
-984,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-985,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is a 1 dimensional data structure like 1-D array  link list ,1.5
-986,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure., Data structure where data elements are arranged linearly where each and every element is attached to its previous and next adjacent is called a linear data structure .,2.5
-987,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is basically the data structure in which the static memory is used to allocate space in the memory.\nex- arrays. linked list,2.5
-988,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Data structures that use linear time complexity or we can say 0(n) this time complexity's to evaluate any algorithm . ,2.5
-989,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is the place which stores the data in a linear or single form.,2.5
-990,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure include arrays ,2.5
-991,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a data structure which stores value in a single order are called linear data structures .\nex: array.,2.5
-992,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Storing data/values in consecutive manner for example Array, Linked List.",2.0
-993,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data structures that store the data in a linear format are called linear data structures\n\nExample: array, Linked List",2.0
-994,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"There are many data structures which stores the data in different manners or ways.\nLinear data structures, stores the data linearly in themselves. ",2.5
-995,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structures are one dimention in space just like 1D array .,2.5
-996,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure is the data structure which uses 1 dimension such as list, 1-D array, stack, queue,",2.5
-997,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is a data structure where all the individual elements are arranged in a linear order so traversal and many other operations become very simple. \nIn linear data structures like an array or linked list the elements are connected in a single order without any branches . Every other element is only connected to two other \nelements unlike graphs and trees.,2.5
-998,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a linear data structure is one where the elements are arranged in a linear order. Examples of linear data structure- linked list,2.0
-999,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Data Structures are basic unit to develop an algorithm.Data Structures which can be solved in linear time complexities are termed as Linear data structures. If the algorithm carries time and space complexity of O(n) it can be classifies under linear data structures. ,1.5
-1000,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are those data structures which occupy space in memory in form of continuous blocks. Some examples of linear data structures can be array, stacks, queues, etc. ",2.5
-1001,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"liner data structure take  O(n) space  , such as array , linked list , stack and queue .where our most of the operations  take  linear time to give us solution.",2.5
-1002,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Arrays , Linked list, stacks , queues which have elements in one line are linear data structures. searching in them is done in O(n) time. ",2.5
-1003,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A data structure that that has no child nodes(levels or heights) and can be stored in the form of a single 1-d array are called as linear data structures. For ex, linked lists, stacks, queues, arrays etc.",2.5
-1004,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A structure which has no child noes ( Height of the tree is 1), this can be stored in a 1-D array . Examples are linked lists and arrays",2.5
-1005,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure are those data structure which store data in only one direction that is either row wise or column wise. like array ,vector, linked list etc. here we can see the array, linked list store data one by each node of the linked list contain the address of the other and we do nt find two path (either left or right) while traversing from one node.",2.5
-1006,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A data structure that stores data linearly (adjacent to each other) in continuous memory location is known as linear data structure.,1.5
-1007,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure are structures in which the tasks are performed one by one linearly,2.5
-1008,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure  involves simple data structure like sorting ,searching .",2.5
-1009,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure is a data structure which is in linear form in memory and can be traversed linearly in one - direction (parallel and anti-parallel).\nevery memory block is linked to only one memory block.\ncommon examples are array, linked list etc. ",2.5
-1010,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,In data structure tasks are performed linearly one by one.,1.0
-1011,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is a data structure which takes time complexity of O(n)\nFor ex: 1. 1-D Array\n            2. pointers\n            3. linked list,2.5
-1012,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is a data structure in which the elements are arranged linearly. All the elements can be traversed in one go.,2.5
-1013,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,elements are in liner order in linear data structure like array.,2.5
-1014,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"The data structures which can be traversed only at a 1 dimensional plane or they are 1 D in nature are know as Linear data structures .\nEg: 1-D array, Linear Linked List",2.5
-1015,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Liner Data Structure are the data structures which has only one link to the next element.\n1. array.\n2. linked list,2.5
-1016,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"a linear data structure has only a single link to the next element like array, linked list,etc.",2.5
-1017,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A data structure in which the data is stored in a linear format such as an array or a linked list.,2.5
-1018,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Those data structure that has no ambiguity while accessing the next or previous element as they are arranged in a linear format , one after another. are called linear data structure. They have only one element succeeding or preceding them.",2.5
-1019,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure includes arrays, queues, string, linked list. In linear data structure you perform operations like merge sort, bubble sort, quick sort, etc",2.5
-1020,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure is one that stores data linearly. \nE.g.: Array, Linked List.",2.5
-1021,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A data structure which store values linearly one by one as in an array,is known as linear data structure.",1.5
-1022,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are those that stores the data in linear form where a chain like memory is formed in and data are linked to each other in successive format. Example queue, linked list, etc.",2.5
-1023,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is such that that can be implemented using one\nEg: Array, Stack, Queue",2.5
-1024,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is a type of data structure that can be implemented in 1D.\nEx: Array, stack, queue, etc.,",2.5
-1025,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"a linear data structure is a data structure which stores data in 1-D such as arrays, stack, queue, etc.",2.5
-1026,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure are those data structures which works on a single level. They do not form multiple levels. They store the data in 1-D such as queue, stack, etc.",2.5
-1027,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is defined as a type of data structure that stores data or information one after the other linearly. Ex :Arrays,2.5
-1028,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Data structure which stores data linearly is called linear data structure.,2.5
-1029,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Data structure where data elements are arranged sequentially or linearly where each and every element is attached to its previous and next adjacent is called a linear data structure.\n\n,2.5
-1030,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,it is a data structure with linear data.,2.5
-1031,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are basic data structures which do not have children or branches. The elements are inserted in a sequence. Ex-> Array, Linked list, etc.",2.5
-1032,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are the data structures in which the data is stored in a linear 1-D format. In these data can be stored in only a unidirectional way. Eg. - array, vector. ",2.5
-1033,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,the data structure with only 1 hashing involved are known as linear data structure,2.0
-1034,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure are those which occupies linear space in the memory. For example - Arrays, Linked List",2.5
-1035,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are data structure that go in only one direction. You don't have different paths from a particular point. Arrays, linked lists, vectors are linear data structures. Trees, graphs are non linear data structure",2.5
-1036,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure refers to the approach to any solution within linear time complexity. Example: Linear search,2.5
-1037,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,data is stored linearly,2.5
-1038,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is where data is stored linearly with the neighbor element connected to each other,2.5
-1039,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,data structure which have fixed size and we can update it that called linear data structure,2.5
-1040,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structures are those where data is stored in a linear format. example linked list, stacks and queues",2.5
-1041,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A data structure that could be defined in which items are inserted or arranged in a linear order. Example- Array, linked list.",2.5
-1042,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data structures that take the input value linearly and eject the values in the same way are linear data structures. Example of such data structures are as FIFO (first in first out), LIFO (last in first out)",2.5
-1043,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is a type of data structure which perform its operations linearly like insertion, deletion, updating and searching/",2.5
-1044,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.," In  the Linear data structure where data elements are arranged sequentially or linearly where each and every element is attached to its previous and next adjacent is called a linear data structure. In linear data structure, single level is involved.",2.5
-1045,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,it is a data structure in which data is arranged linearly like array and linked list,2.5
-1046,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is one that stores data linearly in the memory. An array is an example of a linear data structure. ,2.5
-1047,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure are the type of data structures in which the elements are stored in sequential memory locations. One can traverse through all the elements knowing only the location of first element in the structure. For example: Arrays.,2.5
-1048,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"data structure that stores data sequentially, after after another\nfor example: queue, vector, lists. etc",2.5
-1049,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure : array ,1.5
-1050,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is one where data items are arranged in a linear fashion. Each member is attached to its neighboring elements. The structure permits single-level data storage because the data elements are stored in a linear fashion. The data can be traversed in one run. \ncertain examples of linear data structure are-stacks, linked lists, queues etc.",2.5
-1051,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,It is a data structure where elements are arranged sequentially or linearly.\nFor example arrays. ,2.5
-1052,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure is a data structure in which the elements or the required data are kept linearly for example:\nArrays, Queue, stack, vector etc.",2.5
-1053,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is a data structure that can only be traversed unidirectionally. Eg: Arrays, linked list, etc.",2.5
-1054,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a linear data structure is a one in which the data is allocated in such a way that we traverse the data in linear fashion like (one after the other) example in an array rather than jumping from one memory location to other. ,2.5
-1055,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data structure where data elements are arranged sequentially or linearly where each and every element is attached to its previous and next adjacent is called a linear data structure. Here can traverse all the elements in single run only. Linear data structures are easy to implement because computer memory is arranged in a linear way. Its examples are array, stack, queue, linked list, etc.",2.5
-1056,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"data structures that store information one after the other in the memory.\neg: array, string",2.5
-1057,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,in which each data are4 connected one after the other ,2.5
-1058,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"In a linear data structure, the elements are stored in a continuous memory space",2.5
-1059,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structures like 1d array,stack,queue",2.5
-1060,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is one where data items are arranged in a linear way. Each member of the data structre is attached to its neighboring element. ,2.5
-1061,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structures:\narray\nlinked list\nstack\nqueue,2.5
-1062,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure stores data in 1D manner.Example-array,vector,stack,queue",2.5
-1063,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a linear data structure is one that stores single type of data such as array,2.5
-1064,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear Data Structure has lesser time complexity ,1.0
-1065,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is arrangement of data where data is arranged in linear way. ,2.0
-1066,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is arrangement of data where data items are arranged in a linear way.,2.0
-1067,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is the structure in which node has only one child or one branch . You can go only in one direction.,2.0
-1068,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is a data structure having elements arranged in a linear fashion \nEg - Array,2.0
-1069,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure are the data structure that performs operation in single dimension such as array , strings , stack , queue and etc.",2.5
-1070,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,having elements arranged in linear fashion.,2.0
-1071,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Data structures that are linear in size like linked lists are called linear data structures,2.5
-1072,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,The data structures in which the data is stored in a sequential order is a linear data structure. For example: arrays.,2.5
-1073,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear as the name only suggest it is arranging something in the linear or the sequntial order \nso in linear data structure the data is arranged in the linear order,2.5
-1074,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,The linear data structure includes:\n1-Stack\n2-Queue\n3-Lists,2.5
-1075,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,all data items are connected in linear manner example array,2.5
-1076,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is the one in which we can iterate through the whole data without without coming back to an element which has already been iterated through. For example, an array, a singly linked list, a hash table. Here the length of the data structure is the number of hops or iterations it takes to reach from the starting element to the ending element.",2.0
-1077,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"like arrays, linked list, that data structure that can be useful linearly in both its work and while iterating ",2.0
-1078,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear Data Structure are Data Structure which have continuous memory for storage\nfor eg. array , linked list  etc..",1.0
-1079,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure means a structure which is connected linearly to each other in some manner . for example an array is connected with indexes starting from zero whereas a linked list unlike array is connected through addresses of a pointer index.,1.0
-1080,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is the data structure where data items are arranged in a linear fashion. Each member is attached to its neighbouring elements.,1.0
-1081,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,data structure in which data is stored in linear way for example array and lists.,1.0
-1082,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,in linear  data items are arranged in a linear way.  and the data can be traversed in one run.,2.0
-1083,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Data Structure In 1 D is known as linear data structure. It stores data horizontally memory blocks. These group of  memory blocks can also be called an Array. A single iterator is used to traverse memory chunk and assign the values to the memory blocks.,1.0
-1084,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure stores data in a one dimensional or linear fashion. Examples of linear data structures are arrays, stacks and linked lists.",1.0
-1085,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"it is a type of data structure in which the data is stored linearly example- array, stack, etc.",1.0
-1086,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is defined as a structure in which the data is arranged in linear order. Ex : array, list\nExample of non linear ds: Trees",1.0
-1087,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are data structures which store data in contiguous memory locations. Eg: Array\nThese can be traversed linearly, in a specific order in which the data is saved. ",1.0
-1088,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a data structure in which the elements are organized in a linear manner.ie. the elements are arranged one after the other in a specific order.\n,1.0
-1089,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is a data structure in which each data value is linearly connected with the one that precedes and suceeds it . ,2.0
-1090,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure are data structures in which data is stored in a line/linear manner. For example- array, vector, list.",1.0
-1091,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,all elements are stored at the same level in linear ds like arrays and linked lists.,1.0
-1092,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is linearly connected to all other nodes of the data structure as in i.e  linked list.,1.0
-1093,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A data structure in which the data is stored one after another such that every element is connected to just one element which is it's intermediate.,2.0
-1094,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.," inputs which are provided to it . There are three types of  notations i.e.,  big O, big Theta (Θ), and big Omega (Ω).",2.0
-1095,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are one dimentional data structures. Eg: Lists, 1-d array ",1.0
-1096,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structures are data structures in which data elements are stored in a linear sequence.\n1 Arrays\n2 linked list\n3 stacks\n4 queue,2.0
-1097,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Arrays, vectors, stack, queues are all linear data structure. We store the elements linearly means in a line and all the algorithms we apply on linear data structure are in a line only like traversal, searching, sorting etc. They have linear time complexity - O(n).",1.0
-1098,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is ,1.0
-1099,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,We can define Linear data structure ->  the adajacents elements in linear data structure are connected lineraly to each other .\nex. Linked Lists,2.0
-1100,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is one which stores data in a linear manner.\nEx-Array, List",2.0
-1101,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is a way of storing data in which storage buckets or connected linearly and no multi-dimensional is present after fully creation of data structure.,2.0
-1102,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are the data structures in only one dimension.\n Some of the examples are 1D array, stack, queue etc.",2.0
-1103,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure stores data in contiguous memory locations which means that they are stored in memory in adjacent memory places.,2.0
-1104,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"a linear data structure is a 1D data structure like a list, array, linked list, queue and many others in which one parameter like height or width is set to one.",1.0
-1105,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"The data structure that use linear structure format like stack ,linked list, queue . These data structure traverse, search linearly.",2.0
-1106,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"The data structures which can imagined to be placed in a line.\nEx, Array, Stack, Queue.",1.0
-1107,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Asymptotic Notations are symbols used to determine the time and space complexity of the algorithms.\nThey are-\n1.) Big Omega Notation\n2.) Big O Notation\n3.) Theta Notation\n4.) Small Omega Notation\n4.) Small O Notation,1.0
-1108,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure refers for finding elements one by one and comparing one by one , not optimal ",2.0
-1109,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"data structures which are uni directional and doesnt have any branching. like 2d arrays, linlked lists.",2.0
-1110,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is linear function who function create linear structure ,2.0
-1111,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure help us into perform some function in a linear format for example we can use linear data structure using array and we can not use linear data structure using matrix ,1.0
-1112,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure is a data structure where all the data can be iterated over linearly, it will take n iterations, n being the size of the linear data structure. All the data is in a line, like in arrays, stacks, queues, lists.",2.0
-1113,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure includes array, linked list etc ",1.0
-1114,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure are termed as array , linked list which has the address of the next node in a linear way, such as we can move to other elements of the data structure in a linear way",2.0
-1115,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is one which has no branches, one node is connected is at most connected to 2 nodes.",1.0
-1116,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"it is a data structure which is linear in size example linked list, arrays",1.0
-1117,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure such as array have only one way implementation  of operation on them . ,1.0
-1118,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is used to store data in a linear fashion as in array where each element is stored as contiguous memory in a linear way.,1.0
-1119,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure are  like  linked list , arrays . that stores values ",1.0
-1120,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is the one that stores the data linearly and there is only one dimension when trying to access the data. For example a single dimensional array, a stack, a queue or a linked list are all linear data structures. As we can move in only one dimension in these data structures. ",2.0
-1121,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,where data items are arranged in a linear fashion,1.0
-1122,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure are the data structures that stores data  linearly or we can say that one by one .For example, suppose any object comes first then we will place it at first position and if after that any object will come then we will place it at second position and so on.\n ",2.0
-1123,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A data structure in which we can traverse in only one direction i.e we don't  have other paths .. eg. arrays , stack , queue.",2.0
-1124,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data Structures in which we can store data linearly is called linear data structure. Examples of linear data structure are arrays, linked list(doubly linked list, circular linked list etc.)",2.0
-1125,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure are the ones that travel in one direction as if walking on a line. for example an linked list is a linear data structure where the traversal happens in one direction unlike a tree where the movement from the root nodes can go in either of the direction of its child nodes.,1.0
-1126,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is which is of linear size eg - arrays, linked lists and only insertion operation is performed.",1.0
-1127,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"It is a data structure which stores the data in linear format that is it assigns a value to every corresponding index in a straight line  .For example: Array ,vector ,etc. ",1.0
-1128,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"liner data structure is a way of storing the data in way that the time complexity is linear while doing operations such as inserting, deleting, traversing. For example arrays.",1.0
-1129,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structures are the data structures that move only in one direction and work linearly eg: arrays. ,1.0
-1130,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure are those data structures which store data in the system on contiguous memory locations(in a linear fashion). The best example of the linear data structure is array which store the data on continuous memory locations and hence it become very easy to access the data in O(1) time complexity and also we can decide according to the row major order.,1.0
-1131,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a linear data structure is a data structure in which data is stored 1 dimensionally eg. arrays stacks queues etc.,1.0
-1132,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,In linear data structure there is no branching among the elements and the traversal is in a linear order where each element is denoted by a specific index for example arrays.,1.0
-1133,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,array is an example of linear data structure,1.0
-1134,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear Data Structure helps stacking data in a linear manner. ,1.0
-1135,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is a data structure which stores data in a single dimension and a single row. eg Array, Vector",1.0
-1136,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is a one which stacks data in a linear manner.  For example, each pointer points only to one particular address of the other data value.\nExamples- Stack, Queue. linked list",1.0
-1137,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure are the part of data structure which deals with arrays in linear form like 1d array ,1.0
-1138,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure are one dimensional data structures elements linked to each other. They are stored in a line that is linearly in the memory for example arrays, stack , queue etc. Levels or hierarchies aren't present. ",1.0
-1139,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structures is a collection of data which can be easily accessed through the use of pointers and indices.,1.0
-1140,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure is a structure which stores data linearly like array, stack, queue etc.",1.0
-1141,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1142,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is the data structure where the data can be iterated linearly,1.0
-1143,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,in linear data structure data or the items are arranged linearly in a straight line                                                                                                                                                                                                                                            ,1.0
-1144,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structures include types like singly linked lists, doubly linked lists , queues, stacks, heaps etc. which avoid a hierarchical structure using a data type. ",1.0
-1145,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure,1.0
-1146,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure are like: arrays strings etc.,1.0
-1147,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is a structure of data where data are arranged in a linearly. Each member is attached to its neighboring elements. This data structure permits single-level data storage and can be traversed in one go.,1.0
-1148,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure in which data is arranged in a linear way.,1.0
-1149,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a linear data structure are the one where data items are arranged in a linear fashion. Here each member is attached to its neighboring element.,1.0
-1150,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A data structure in which the arrangement of data items is in linear order that is one after the other and only one to one that data structure is known as a linear data structure. for ex. array, linked list",1.0
-1151,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"This data structure stores the data in linear fashion like arrays, stacks, queues, etc.\n",1.0
-1152,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A data structure which stores its data in a chain or in linear manner . Exampe-  stack, queue, array",1.0
-1153,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,An example of a linear data structure is an array ,1.0
-1154,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure which we can stored in a linear manner. like array, string etc.",1.0
-1155,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure is one where all data items are arranged in linear fashion i.e in a single row\nfor Eg: Arrays,Strings",1.0
-1156,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structure is a data structure which can be implemented in linear time.,1.0
-1157,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"It is a type of data structure in which time complexity is always constant .  Some of its examples are linear search , binary search.",2.0
-1158,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is data structure in ehich we can traverse from start to end in a sigle direction.,1.5
-1159,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data structure where data elements are arranged sequentially or linearly where each and every element is attached to its previous and next adjacent is called a linear data structure. In linear data structure, single level is involved. Therefore, we can traverse all the elements in single run only. Linear data structures are easy to implement because computer memory is arranged in a linear way. Its examples are array, stack, queue, linked list, etc. \n",1.5
-1160,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a data structure in which data is stored only in one direction such as array,2.0
-1161,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structures are those data structures that share continious memory location\n example of linear data structure is array,2.0
-1162,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure is in which we form operations in 1d sequence and it types are array,string and many linear data structure ",1.0
-1163,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Asymptomatic analysis gives us an a definite view on the Time complexity of an algorithm. Various different functions depending on how many times they run. increase the time complexity. Asymptomatic analysis gives us a mathematical view to define the worst and best case scenarios on the efficiency i.e. Time complexity and space complexity of an algorithm. Ex- O(n),Theta(n).O(n^2).etc",1.0
-1164,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,It has data elements arranged in sequential manner and each member is connected to its previous and next element.,1.0
-1165,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures like lists - arrays, linked lists etc. These are basically storage data structures which store multiple values while utilizing space in linear pattern, repeating at fixed intervals .i.e. according to its fundamentals.",2.0
-1166,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A liner data structure stores data linearly in a sequence.\nExamples of linear data structure: \n1) Array\n2) Linked List\n3) Stacks\n4) Queues,2.0
-1167,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure are the primitive dat structures present in the respective programming language using which other data structure are implemented or generated.\nfor eg, array, linked list etc.",1.5
-1168,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,The data  structures having a linear sequence.The linear data structures we have studies are as follows:\nArray \nLinked list\nStacks\nQueues\n,1.0
-1169,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,These are the primitive data structure which can be used to generate new data structure.\neg : array ,1.0
-1170,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1171,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure are structure like array in which data is stored linearly and can be accessed using index or pointers,1.0
-1172,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"is a base for all the other data structures, for example, array is a data structure from which we can derive other data structures",1.0
-1173,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structures are arrays. They are the base for complex data structures. ,1.0
-1174,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"in linear data structure the data in connected or stored in linear fashion.\nex. linked list, array etc.",1.0
-1175,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a linear data structure is type of data structure in which the elements are connected in a linear fashion. Each element is connected to only one element which is generally the next element. Examples are-linked list.,1.0
-1176,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"a linear data structure is a type of data structure in which data is stored in a connected fashion,i.e. linearly.for example- linked list.",2.5
-1177,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data structures which store data in a linear manner are called linear data structures.  example stacks, queues",2.5
-1178,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"In linear data structure the data is stored one after another so if we have to go to last data we have to go through every stored data eg-array,linklist.",2.5
-1179,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1180,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data structure that can be traversed in a linear order, example arrays, linked lists.",2.0
-1181,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"The data structure which does one thing that is to store or pass the data. Examples of linear data structure is array, however linked list is not a linear data structure as it not only store the data pass also stores the information of the next the node.",1.0
-1182,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,These are the data structures which stores data in continuous memory blocks. example: array,2.5
-1183,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structures such as array ,2.0
-1184,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear Data Structure is used for searching linearly ,1.0
-1185,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure is a 2d type data structure \nfor example array,linked list",1.5
-1186,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Data structures that occupy continuous memory locations in RAM are called linear data structures. Ex stack, queue. ",2.5
-1187,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is the data structure which stores data in a linear fashion for example arrays, linked lists, stack, queue etc.",2.5
-1188,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1189,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,a linear data structure is one dimensional data structure  which generally save the data in consecutive blocks of memory ,2.5
-1190,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structures are those data structures that creates a linear system of usage, for example linked list is a data structure that contains a node which is made of a data variable and a pointer variable associated to it which gets used to linked it to an another node. The visual representation of a linear data structure is a non loop containing structure that allowes traversing either of the two sides and not randomly.",2.5
-1191,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Array, vector, Linked List are Linear Data structures.",1.5
-1192,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is in which that occupy continuous memory location in ram ,2.5
-1193,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure., Data structures which store the data in continuous or simultaneous memory locations are linear data structures.,2.5
-1194,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"list,\nqueue\nstack\nlinear data structure is one in which the data structure is only in 1d\n",2.0
-1195,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"In linear data structure the collection of nodes is linear, data is present in more linear form",1.5
-1196,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure means data structure in 1D form\ni.e int, double, char, string etc",1.0
-1197,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Arrays , linked list , stack , queue are the linear data sturcutre in which data is in linear form i,e in sequence not in graph form.",2.0
-1198,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1199,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linked list ,sorting  , searching are the linear data structure",1.0
-1200,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure are such as tree graph array \nthese are know as linear data structure ,0.0
-1201,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Linear data structures are data structures arranged in a line-like structure where the elements are linked together in a linear(line) manner. \nSome of them are:-\n1. Arrays\n2. Linked Lists\n3. Queue,2.5
-1202,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linear data structure are those in which data is entered in linear  form for ex - stack, queue ",2.0
-1203,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1204,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is a data structure in which a a data structure in linear form,1.5
-1205,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure reffers to the  sorted structure whose searching complexity will be O(n). ,1.5
-1206,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,Arrays and Linear lists are linear data structures used for storing data in a linear (line-wise) fashion\nFoe e.g.: arr[5]: In this data is stored from Indexes 0 to 4.,2.5
-1207,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear data structure can be traversed linearly like lists, arrays, etc.",1.5
-1208,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is linked list etc,1.0
-1209,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1210,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,A linear data structure is a data structure which is continuous in nature. An example of the linear data structure is an Array.,2.0
-1211,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"Linear  data structures are the those arrays, stacks and queues, heap.",1.0
-1212,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,arrays- used to store data ,0.5
-1213,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,,0.0
-1214,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"A linear data structure is a one dimensional structure.\neg. queue, stack, array.",2.0
-1215,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is used to store the data in a data base in the form of different variable ,0.0
-1216,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"data structures which are arranged in a line structure are linear data structure.\nsome examples - queues, stacks. arrays.",2.5
-1217,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structure is saving in memory continues way that way  linear data structure like array ,2.5
-1218,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,"linked list, array, stack ,queue etc.",1.0
-1219,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,the data structures which are not derived from another data structures like \n1)array\n,1.5
-1220,What is linear data structure?,A linear data-structure has sequentially arranged data items. The next time can be located in the next memory address. It is stored and accessed in a sequential manner. Array and list are example of linear data structure.,linear data structures can be the processes in which time complexity is very small,0.0
-1221,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching , sorting, adding elements, deleting elements",2.5
-1222,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"operations that can be performed on a data-structure are insertion, deletion, searching, merging, sorting",2.5
-1223,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"common operation that can be performed on a data structure are insertion ,deletion ",2.5
-1224,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"1) Insertion, 2) Deletion, 3) Searching and Retrieving, 4) Sorting, 5) Updating",2.5
-1225,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion- at start, at end, at given index.\nUpdate- change the value of a given key to new value\nDelete- delete a particular element from the data structure.\nSort- sort the data present inside a data structure\nSearching- searching a given value inside the data structure.",2.5
-1226,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on a data structure is searching ,deletion, update,  retrieving, inserting",2.5
-1227,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,commom operations that can be performed on data structure are :\nsearching \nsorting \ninsertion .,2.5
-1228,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,. Seaching\n. Sorting\n. insertion \n. deletion,2.5
-1229,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a data structure are insertion, deletion, swaping, traversing, searching, sorting and many more. ",2.5
-1230,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1. traversing\n2. insertion\n3. sort\n4. update\n5. stack,2.5
-1231,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations are as follows-\n1. creation \n2. implementation\n3. Display,2.5
-1232,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations that can be performed on a data-structure are :-\n1) Insertion \n2) deletion \n3) selection \n4) sorting \n5) Merging ,2.5
-1233,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations are storing data, transferring data, retrieving data, insertion deletion in  the given data.",2.5
-1234,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a data structure are\nAddition of data\nDeletion of data\nTraversal of data\nStoring data in a apt, easy to access format ",2.5
-1235,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operation that can be performed on data structures are :\n- traversal\n- deletion\n- insertion,2.5
-1236,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common Operation on the data Structure :-\nInsertion, Deletion, Traversal, Display, Sorting.",2.5
-1237,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1. Insertion Of data\n2.Searching for data\n3. Retrieval of data if required data found after searching\n4.modifying the data,2.5
-1238,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,,0.0
-1239,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"some operations like searching ,sorting, insertion ,deletion, heapify,  construction of trees ,forming recurrences ,finding shortest path of all in graph",2.5
-1240,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on data structure:\n1)Searching: Linear Search, Binary Search, Median Search\n2)Sorting: Bubble Sort, Merge Sort, Insertion Sort, Selection Sort\n\n",2.5
-1241,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching , Insertion , Deletion , Dividing , Merging , Reversing ,Traversal.",2.5
-1242,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"the common operations that can be performed on searching , sorting , deleting ,merging, traversals etc.",2.5
-1243,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operations performed on data structres :\ninsert: used to add particular element into the data structure\ndelete: used to delete a particular element from the data structure\n\n,2.5
-1244,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations that can be performed on a data structure are :\ni) traversing\nii)insert\niii) delete,2.5
-1245,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion  , deletion , traversing , reversal , search",2.5
-1246,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Some common operations are traversal , insertion and deletion.",2.5
-1247,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, Traversal, Sorting, Insertion, Deletion etc",2.5
-1248,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, insertion, deletion, sorting, Traversal, etc.",2.5
-1249,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,the common operation that can be performed on data structure are :\n1) pushing an element in the provided data structure.\n2)popping the element from the given data structure.\n3)getting the first and last element from the given data structure.\n4)Getting the address of the element from the given data structure.,2.5
-1250,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a data structure: insertion, deletion, searching, sorting (in some data structures).",2.5
-1251,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Some common operations which can be performed in data structure are searching and sorting like depth first search or breadth first search, we can calculate minimum cost problems, minimum /maximum distance problems, travelling salesman problem, inserting an element, deleting an element and so on",2.5
-1252,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1) inserting \n2)deletion\n3)searching \n4)sorting\n,2.5
-1253,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,the common operations that can be performed on any data structures are :-\nsearching an element\nextracting an element \ninserting an element,2.5
-1254,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching, sorting ,depth first search and breadth first search ",2.5
-1255,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Some common operations that can be performed on a data-structure are :\ninsertion, deletion, sorting",2.5
-1256,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"adding an element, removing an element, searching or sorting in data structure.",2.5
-1257,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,searching and sorting are common operations that can be performed on a data-structure.\n,2.5
-1258,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,The common operations are :\nsearching and sorting ,2.5
-1259,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations that can be performed on a data-structures are:\n1. searching \n2. sorting.,2.5
-1260,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,various operations include:-\nsearching\nsorting \ndeleting\ntraversing\nadding or inserting,2.5
-1261,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, sorting, insertion, deletion are some of the common operations that can be performed on a data-structure.",2.5
-1262,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"A data structure can be used to store, search and retrieve and delete if required, any piece of information. Various data structures can be used in various situations to perform the mentioned operations.",2.5
-1263,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Sorting, searching, data storage and operations like arithmetic operation etc. ",2.5
-1264,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Some common operations that can be performed on data structures are:\n- Insertion\n- Deletion\n- Sort\n,2.5
-1265,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Various operations that can be performed on a data structure are arithmetic operations , boolean operations , relational operations like or( | | ),and( && )",2.5
-1266,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a data structure are:\n1. Insertion\n2. Deletion\n3. Updating\n4. Calculation, etc.",2.5
-1267,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operation carried out on Data-Structure are storage, insertion, deletion, and calculation or computation.",2.5
-1268,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations that can be performed on data structure are:\nInsertion\nDeletion\nSelection\n,2.5
-1269,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1)Searching\n2)Sorting\n3)Deletion\n4)Insertion,2.5
-1270,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations such as searching, sorting, insertion, deletion.",2.5
-1271,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Data structures are used to store data and hence the common operations performed on these are storing the data, searching the element and keeping the record by adding or deleting nodes.",2.5
-1272,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common Operations on data structures.\n1. Traversal\n2. Insertion\n3. Deletion\n4. Update,2.5
-1273,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Traversing ,Searching ,Sorting, Deletion ,Insertion",2.5
-1274,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching, sorting, deleting, inserting, merging etc.",2.5
-1275,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insert, Delete, Update are the most common operations",2.5
-1276,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion, deletion, and searching.",2.5
-1277,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"implementation of linked lists, stacks, queues, graphs, and various types of trees including heaps ",2.5
-1278,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations that can be performed on a data-structure are:-\n1. Insertion\n2. Deletion\n3. Searching\n4. Finding and extracting minimum and maximum,2.5
-1279,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Accessing the elements\nInsert\nDelete\nUpdate\nDisplay,2.5
-1280,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1)Insertion\n2)Updation\n3)Deletion\n4)Searching,2.5
-1281,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"add data , remove data , find frequency of element , add two together , convert from one data struct. to another",2.5
-1282,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Searching Sorting Insertion\nDeletion,2.5
-1283,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion , traversing and deletion operation ",2.5
-1284,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operations that can be performed on a data structure are\n1.input\n2. output\n3. search,2.5
-1285,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that could be performed are insertion, deletion, union , find min or max.",2.5
-1286,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Operations that can be performed on a data structure are searching anything in data, sorting the data accordingly (increasing or decreasing), Insertion of elements into the data structure or deletion of elements from the data structure.",2.5
-1287,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a data-structure are insertion, searching, sorting, deletion , updating etc.",2.5
-1288,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,The  operations are :-\nINSERTION\nDELETION\nUPDATION ,2.5
-1289,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion ,deletion, update ,modify ",2.5
-1290,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,The following operations can be performed on a data structure: \n1. Insertion\n2. Deletion\n3. Search,2.5
-1291,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, Insertion, Deletion, Sorting are some common operations that can be performed on a data structure. ",2.5
-1292,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion, deletion, iteration",2.5
-1293,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations performed on a data structure are:\n1. Insertion \n2. Deletion\n3. Searching\n4.Modification\n,2.5
-1294,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching,sorting,insertion,traversal,deletion etc.",2.5
-1295,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, sorting, traversal etc. ",2.5
-1296,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Following are the common operations that can be performed on a data-structure:\n->Insertion\n->Updation\n->Deletion\n->Traversal,2.5
-1297,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on a data structures are sorting, storing, merging, insertion, deletion, etc.",2.5
-1298,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,THE common operations that can be performed on a data structure are \n1 deletion\n2 union\n3 traversal\n4 searching \n5 sorting\n,2.5
-1299,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations are:\n1. Traversing\n2. Searching\n3. Sorting\n4. Insertion \n5. Deletion,2.5
-1300,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,we can add or delete items. we can traverse the data structure ,2.5
-1301,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,,0.0
-1302,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1. Insert Data\n2. Delete Data\n3. Move Positions\n4. Edit,2.0
-1303,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operations are adition subtraction multiplication accessing the elements,2.5
-1304,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,some common operations that can be performed on data structures are:,2.5
-1305,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"common operations like insertion merging searching , sorting can be performed on a data structure",2.0
-1306,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,we can perform insertion deletion and searching operations on a data structure,2.0
-1307,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,insert or store data\ndelete\nupdate data\nsearch an element stored in data structure,2.0
-1308,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Insertion\nDeletion\nSelection\nSearching,2.5
-1309,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, Sorting are the operations that can be performed on a data structure. ",2.0
-1310,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations are:\nSearching, Sorting, insertion of new data.",2.5
-1311,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion, deletion , finding maximum and minimum element.",2.0
-1312,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Sorting\nSearching\nTraversing,2.5
-1313,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,The common-operation that can be performed on a data-structure are taking input and printing outputs.,2.5
-1314,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching, sorting, inserting, deleting",2.5
-1315,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion and deletion at any position , finding element are common operations that can performed on a data structure",2.5
-1316,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Following operations can be performed on a data structure:-\n1. Insertion\n2. Deletion\n3. Traversal\n,2.5
-1317,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,operations performed are searching sorting insertion,2.5
-1318,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,The common operations that can be performed on a data-structure are:\n1-Traversal\n2-Insertion\n3-Deletion\n4-Searching,2.5
-1319,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Search , Insert , Delete",2.5
-1320,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, sorting, heapify or then making it to a min heap or a max heap, get minimum, get maximum",2.5
-1321,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"adding, deleting a particular thing, updating, forming its structure\nwell like trees in case of trees we can add a node and delete a node \ncan be done using traversing a node, here a node can be anything that you wants to represent.\naccording to our need we can perform various operations.",2.0
-1322,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,->Insertion\n->Deletion\n->Updating\n->accessing data,1.0
-1323,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,searching\nsorting\npossible shortest path and many more,1.0
-1324,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations on a data structure are traversal,insertion,deletion,searcing,sorting and merging.",1.0
-1325,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,insert-insert the element\ndelete-delete any  element\nupdate-update element with another element,1.0
-1326,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting �� arranging data items in a pre-defined sequence,sorting and searching.,1.0
-1327,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations such as remove and  insert can be performed on data structure. Commands used in STL to this is pop and push ,1.0
-1328,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on data structures are insertion, deletion, updation, searching etc.",2.0
-1329,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"common operations that can be performed are insertion, deletion, updation.",1.0
-1330,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Insertion\nDeletion\nsearching\nSorting,2.0
-1331,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on data structures are Searching , Sorting and traversal. Some types of searches are: Binary search, linear search, etc. Some types of sorting are: selection sort, merge sort , quick sort, etc. Based on the time complexities of these algorithms in the best and worst case , the appropriate searching , sorting algorithm is chosen.\n",2.0
-1332,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,searching \nsorting\ninsertion deletion,2.0
-1333,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1) Traversal/Printing\n2) Delete\n3) Insertion (In most cases at any particular position too)\n4) Updating \n,1.0
-1334,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Sorting Searching,1.0
-1335,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1. Insertion\n2. Deletion\n3. Traversal,2.0
-1336,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"operations that can be performed are: searching, sorting , deleting , input of nodes etc.",2.0
-1337,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations include entering data, deleting data ,searching, sorting, merging",2.0
-1338,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Linear data structures are those stores data in a linear manner such as array ,vector, stack ,queues , in these the data in stored one by one .",2.0
-1339,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Insertion \nDeletion\nSearching ,2.0
-1340,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Traversing \nsearching\ninsertion \ndeletion\nupdate,2.0
-1341,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"1. We can find sum of an array\n2. Maximum Element & Minimum Element\n3. Searching - Binary Search, Linear Search, Interpolation Search, Median Search\n4. Sorting - Selection Sort, Bubble Sort, Merge Sort, Quick Sort, Heap Sort",2.0
-1342,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"common operations that can be performed on data structures are sorting ,searching etc.",2.0
-1343,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1. Deletion\n2. Insertion\n3. Transversal,1.0
-1344,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Operations-Insert, Delete, Update",1.0
-1345,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Sorting, Searching, pop, push, delete, extract min, extract max, transversal, bi-directional pointing ",2.0
-1346,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Their are many operations that can be done on different data structures such as :\n1 . Traversing \n2. Searching\n3. Insertion\n4. Deletion,1.0
-1347,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Finding min and max data, extraction of particular data or min or max element, sorting of data in particular order, ",2.0
-1348,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,some common operations that can be performed on data structures are:\n1. insertion\n2. deletion\n3. swapping\n4. modification\n5. searching\n6. sorting\n7. printing\n8. traversing,2.0
-1349,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion, deletion, traversal, searching.",1.0
-1350,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on a data-structure are insertion, deletion and replace.",1.0
-1351,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Linear Data structures stores the data in a linear form.\nFor example- arrays, list, etc.",1.0
-1352,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"operations performed on data -structure :\ncomparing , finding best optimise solution , decreasing time complexity, finding optimal path on map, graph coloring , string matching ",1.0
-1353,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,operations like input and output of data. storing and searching required data. deletion and replacement of data.,2.0
-1354,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"sorted, merge,  push back, create function, insert, tree, array, stack, graph, searching",2.0
-1355,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"we can perform search ,sort ,insertion operation and many more on a data structure",1.0
-1356,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Some of the common operations that can be performed on a data structure are pop or remove or erase(which can be used to delete elements), push, insert(which can be used to add elements), size( which can be used to access the size of the data structure), empty( to check whether the data structure is empty or not).",1.0
-1357,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,The operation are \nInserting the element \nDeleting the element\nSearching the element,1.0
-1358,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,The common operations which can be performed on data structure are \nInsertion \nDeletion\nSearching,2.0
-1359,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion,deletion, search.",1.0
-1360,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion, deletion, rotation are some operations that can be performed on a data structure",1.0
-1361,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion of elements into them ,deletion of elements ,sorting of elements, swapping of elements",2.0
-1362,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,search\ndelete\ninsert\nsort,1.0
-1363,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operation that can be performed on data structure are :-\ninsertion \ndeletion \n,1.0
-1364,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The most common operations that are performed on any data structure are traversal, inserting an element, deleting an element, modifying an element.",1.0
-1365,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1.Insertion\n2.Deletion\n3.Sorting,1.0
-1366,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The operations that can be performed on a data-structure are deletion , insertion and updating .",1.0
-1367,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,searching and sorting.,1.0
-1368,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"There are various data structures. Most simply to start with array, linked list, stacks, queue, heap and much more. Operations such as adding new element, deleting an old element, changing the value of the particular element, printing all elements are operations that could be performed in a data structure.",1.0
-1369,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,insertion\ndeletion\nsearching\ntraversal\n,2.0
-1370,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a data structure are insertion, deletion , finding of an element , etc. ",1.0
-1371,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Generally, common operations include insertion, deletion, updation, searching etc.",1.0
-1372,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a data structure are: Inserting, deleting, traversing, searching.",1.0
-1373,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion, deletion, searching, sorting are the common operations that can be performed on a data structure.",1.0
-1374,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The most common operations that can be performed on a data structure are -\nSearching an element, \nSorting the data of that data structure in a perticular fashion like in ascending order and in descending order,\nDeletion of an particular element or more than one elements from the given data structure.\nInserting an element in the given data structure.\nUpdation in the given data value in a data structure.\nAccessing the element from front or back also deletion of an element from both front and bachl.\n",1.0
-1375,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,insertion\ndeletion\nupdation\ntraversal,1.0
-1376,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal �� accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations which can be performed are:\nInsertion , Searching And Deletion of an element in a data structure.\n",1.0
-1377,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, sorting, finding a pattern,etc.",1.0
-1378,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on a data structure are linking a list, gaming program, searching and sorting. ",1.0
-1379,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations that can be done in a data structure are:\n1. Sort\n2. Search\n,1.0
-1380,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations and can be performed on data structures are- searching, sorting, merging, partition, etc.",1.0
-1381,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operations performed on data structure are  \nsorting\nbacktracking\nrecursion  ,1.0
-1382,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on data structures are insertion, deletion, updating, traversals, searching and more.",1.0
-1383,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion, deletion, sorting, searching",1.0
-1384,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"size, to check if empty, searching, sorting, deletion, insertion and updating are some commonly used operations on data structures.",2.0
-1385,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching, sorting, insertion, deletion",2.0
-1386,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Stack, Queue, Searching ,Sorting , Deletion, Insertion, Traversal, Pointers , etc. can be performed on a data structure.",1.0
-1387,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion, Deletion, Searching ,Sorting are commo n operations performed on a data-structure.",1.0
-1388,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching , sorting , traversal etc.",2.0
-1389,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on data structures are:\na)add,subtact\nb)print\nc)max min\n",1.0
-1390,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common Operations that can be performed on a datastructure are :insertion deletion searching etc.,1.0
-1391,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,,0.0
-1392,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"search operation , sort operation , insertion etc. can be commonly performed on a data structure",1.0
-1393,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operation that can be performed in data structure are:\n1)searching \n2)sorting\n3)insertion\n4)deletion,1.0
-1394,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"You can change the shape, structure and order of data items, you can insert, delete and modify the data items in  a data structure.",1.0
-1395,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Sort, insert, push, pop",1.0
-1396,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Some common operations are:\nSorting\nMerging\nSearching\nInsertion\nDeletion,1.0
-1397,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on an data structure are traversal , insertion ,deletion opertations .",1.0
-1398,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"common operation performed on data structures are sorting, searching, traversing, storing etc. ",2.0
-1399,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching, sorting, insertion, deletion ,object creation etc.",2.0
-1400,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations include insert, delete, sort, etc.",2.0
-1401,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"There are a number of operations which can be performed in data-structure such as insertion , deletion , addition , subtraction , multiplication division and so on.",1.0
-1402,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insertion , deletion ,replacing, search an element in it ,sort the element.",1.0
-1403,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Traversing: Traversing a Data Structure means to visit the element stored in it. It visits data in a systematic manner. This can be done with any type of DS. \nSearching: Searching means to find a particular element in the given data-structure. It is considered as successful when the required element is found. Searching is the operation which we can performed on data-structures like array, linked-list, tree, graph, etc.\nInsertion: It is the operation which we apply on all the data-structures. Insertion means to add an element in the given data structure. \nDeletion: It is the operation which we apply on all the data-structures. Deletion means to delete an element in the given data structure. \n\n\n",1.0
-1404,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insert an element ,search for an element ,remove an element",1.0
-1405,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence, common operations that can be performed on a data-structure are \nInsertion\nDeletion \nupdation\n,1.0
-1406,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion��− removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operations that can be performed is insertion and deletion,1.0
-1407,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Asymptomatic notations are mathematical representations to define or assign the complexity and efficiency of any algorithm. \nThere are mainly 3 - \nBig O notations , ex - O(n)\nTheta notations \nOmega Notations ",1.0
-1408,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1)Searching\n2)Sorting\n3)Insertion,1.0
-1409,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,insertion.\ndeletion.\nsize.\nto check if its empty.,1.0
-1410,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Operations performed on a data-structure: \n1) Sorting\n2) Searching ,1.0
-1411,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operation are;\n1.Insertion;\n2.Deletion;\n3Retrieval;\n4.Updation.,1.0
-1412,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on a data structure are searching,sorting,",1.0
-1413,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence," Searching, Sorting and Traversing.",1.0
-1414,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Insert: To add/insert a new value in the structure (for example: inserting new values in Linked Lists)\nDelete: to delete a pre-existing or user-inserted value\nEdit: To edit any inserted value.\nUpdate: To replace the original value with a better or later/newer one. (used in: MATLAB version of an SR Latch),1.0
-1415,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,we can delete the element\nwe can search the element\nwe can insert or update the data structure,1.0
-1416,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,common operations that can be performed on a data structure are:\n1)searching\n2)sorting\n3)traversing,1.0
-1417,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"data is stored. operations such as searching, sorting in ascending or descending order, returning maximum or minimum element, traversaL can be performed on data structures,",2.0
-1418,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1. storing the elements\n2. searching the elements\n3. deleting the elements,2.0
-1419,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on a data structure include storing the elements, searching for any element, deleting the element.",2.5
-1420,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"common operations that can be performed on a data-structure are searching, sorting,insertion ,deletion,insertion and deletion at end /beginning/or at a particular position,merging,dividing,etc..",2.5
-1421,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"add element to the data structure (push), delete element from it (pop),  finding the top element etc",2.0
-1422,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operators are-sorting, searching , insertion, deleting. ",2.0
-1423,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Like creating a node ,linked list ,and making tree for searching the node ,using bfs and dfs,\nmain is for sorting and searching that have many techniques for solve them.",2.0
-1424,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"traversal, sort, search, insertion, deletion",2.5
-1425,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Many common operations can be performed on a data-structure like searching, sorting, traversing etc.",2.0
-1426,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Inserting elements, Deleting elements, traversal on elements in data structure",2.0
-1427,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on data structures are insertion, deletion ,update and sort.",2.0
-1428,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Common operations that can be performed on a Data Structures are Insertion,Deletion,searching,sorting etc",2.0
-1429,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,searching\nsorting \nare common operations that can be performed on a data structure,1.5
-1430,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Some common operations are: Searching and sorting.,1.5
-1431,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"The common operations that can be performed on a linear data structure are we can insert new data, pop a value, replace a paricular value by another value,swapping two values, deletion etc.",2.0
-1432,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,additon updation and deletion of data,2.0
-1433,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,the common operations are push and pull ie insertion or retrieval  of data. then there is sizeof operation which is to know to size of data structure. isfull() and isempty() structures are also used commonly.,1.5
-1434,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Some common operations that can be performed on a data structure includes insertion into it, deletion and traversal through it. Sorting can also be done on the member items of a data structure.",2.5
-1435,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Push\nDelete\nFront\nTop\n,1.5
-1436,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching , sorting are the common operation ",1.5
-1437,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1. Insertion\n2.Deletion.\n3.updation.\n4.Searching .,2.0
-1438,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,insertion\ndeletion\nsearching\nsorting,2.0
-1439,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,,0.0
-1440,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"common operations that can be performed are addition(+), multiplication(*), subtraction(-), division(/) of numbers into one another \ninsertion and deletion of inputs etc ",0.0
-1441,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion , Deletion , Sorting , Searching are the common operation thar can be performed on data structure.",2.0
-1442,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Searching,Sorting,Insertioin,Deletion are some of the operations that can be performed on a data structure.",2.0
-1443,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Insertion, deltion,traverse",2.0
-1444,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,sorting searching ,1.5
-1445,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Some of the common operations that could be performed on a data structures are:-\n1. Insert:- To insert a value in the structures.\n2. Delete:- To delete the value from the structures.\n3. Traverse:- To visit the elements and values on the structure.\n4. Edit:- To edit the values of the structures.\n5. Store:- The data can be stored in the structures.,2.5
-1446,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insert , delete , findmin , extractmin , sorting , searching",2.5
-1447,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,,0.0
-1448,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,searching\nsorting\nmerging\n,2.0
-1449,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"it can be insertion , deletion ,replacement etc...",1.5
-1450,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Inserting\nSearching\nSorting\nDeleting\nExtraction,2.0
-1451,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insert, delete,etc.",2.0
-1452,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"searching, sorting",1.5
-1453,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,data storing and searching can be performed on a data structure.,1.5
-1454,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Common operations performed are :\nadd() : to add an element\npop(): to delete an element\nisempty(): to check if the data structure is empty\nisfull(): to check if the data structure is full,1.0
-1455,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"Some common operations the can be performed on a data - structure are searching,  insertion, deletion.",2.0
-1456,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,searching \nsorting \nmerging\ndividing\ntraversing,2.5
-1457,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,insertion \ndeletion \ntraverse\nsorting ,2.0
-1458,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,Insertion\nDeletion\nTraversing\nSearching,2.5
-1459,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"insert , delete , search ",2.0
-1460,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,,0.0
-1461,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1 modified\n2 insert\n3 delete\n\n,2.0
-1462,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,linear & non linear.,0.0
-1463,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,1)insertion \n2)deletion \n3)traversal,2.0
-1464,What are common operations that can be performed on a data-structure?,The following operations are commonly performed on any data-structure – (1)Insertion − adding a data item (2) Deletion − removing a data item (3) Traversal − accessing and/or printing all data items (4) Searching − finding a particular data item (5) Sorting − arranging data items in a pre-defined sequence,"operations that can be performed on data structures are Boolean , algebra, matrix multiplication ",0.0
-1465,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Algorithms are developed by first analysing the problem, then looking for a solution and then modifying our solution as to make sure the time and space complexity conditions are met.",1.0
-1466,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,firstly we develop a pseudocode for the algorithm also known as naive approach and then we proceed to the development of the algorithm by determining the time complexity of the algorithm ,2.5
-1467,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"there can be many approaches to develop an algorithms we can use approaches like greedy algorithms , dynamic programming and divide and conquer ",2.5
-1468,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Algorithms can be developed easily by following this methodology: \n1) Brute Force: Uses the maximum amount of loops and comparisons to find the solution\n2) Divide and Conquer: Divide problem into subproblems and then solve each subproblem to get the solution\n3) Backtracking: Traverse through every available solution if it matches the correct solution continue else backtrack to the previous step\n4) Greedy: Find the local Optimal solution(can have multiple answers to a single problem)\n5) Dynamic Programming: Find the most optimal solution by using memoization,2.5
-1469,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Brute Force: Direct approach to solve given problem. Usually it is not the optimal solution and is not efficient in terms of Time complexity or space complexity.\nDivide and Conquer: It involves dividing the problem into smaller subproblems then solving the sub problems and joining the solutions back together to solve the initial problem for example in merge sort.\nBacktracking: It involves considering all the possible outcomes of a particular step in a given problem. We then consider each of the possibility and check whether it is correct and it can be part of the solution or not. If it is then we move further else we go back and consider a different solution to the previous step. We do this till we acheive the final outcome.\nGreedy Approach: It involves considering the best available solution at every step. This may not give the optimal solution always.\nDynamic Programming: It involves initalising various intial values and then using these initial values to find the next values. This is done using matrices.,2.5
-1470,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Algorithms are develop to solve a problem in a good way according to its time and space complexity .\nFirstly the basic approach is Brute force search where we don't consider time and space as a barrier. our only aim is to find solution of a problem.\nSecondly there are algorithms like divide and conquer and many more which always tried to solve the problem in a less time and space complexity than brute force algorithms.\n,2.5
-1471,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1472,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approaches to develop an algorithms are :-\n. Greedy approach\n. Divide and conquer\n. Backtracking \n. Dynamic programming,2.5
-1473,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,There are many approaches used to develop algorithms\ngreedy approach - it is used for optimization purposes it not necessarily gives the best solution but the solution it gives takes less time and space\ndynamic programing - it is also used for optimization problems but it check every solution available and always gives the best solution\nbacktracking \ndivide and conquer- it divides the large problem into similar sub problems and solve them first and then combine them to solve the larger problem,2.5
-1474,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1475,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approaches are as follows-\n1. analyses of the given problem\n2. Thinking of the most suitable data structure which could solve the problem\n3. Build the naive approach to tackle the problem first\n4. find the best approach that could solve the problem in less complexity(space and time) than other .,2.5
-1476,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approach to develop an algorithm is done in the following steps  :-\n1) study the problem theory carefully\n2) Try to find the solution of the problem using calculation .\n3) Observe the solution and use notations to get the time complexity \n4) optimize you solution in terms of time and space complexity\n5) write the algorithm in a easy manner and implement it on the terminal ,2.5
-1477,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Algorithms can be written by first understanding the given problem, then firstly taking the input values, then applying the basic idea and solving the problem, writing it in steps form.\n1.)write the basic  solution.\n2.) take the input.\n3.) write the easiest n understandable algorithm.",2.5
-1478,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approach to develop an algorithm include understanding the problem thoroughly and then suggesting a solution which satisfices the criteria of having suitable time complexity (ideally linear) and consuming minimum required external storage.,2.5
-1479,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,different approaches are :\n- divide and conquer - understand the problem break it into sub problems and then solve the subproblems and then step by step move to the main problem\n- greedy approach : in each step find the best possible solution for the time being.,2.5
-1480,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,- Understand the problem and break it into smaller problem.\n- Write steps to solve the smaller problem (irrespective of any programming language).\n- Optimise your solution and write steps for bigger problem.,2.5
-1481,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"1. start with what you have - the input\n2. identify the end goal - the output\n3. now identify is there any way you can use the basic algorithms(like for ex binary search, LCS, LIS, etc) to get the desired output\n4. now write the intermediate steps that can be used to take you to desired results",2.5
-1482,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"In order to develop thr algorithm, we must;-\n1. First input all the information you have been given\n2. Predict the output by dry-running it.\n3. \n",2.5
-1483,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"1.predict and understand the problem statement .\n2.find the best suited data structure that can be used.\n3.develop all edges, base cases form recurrences if needed, function needed.\n4.analise the time constraints set the limits of the function accordingly .\n5.check if the time and space complexity can be reduced further.",2.5
-1484,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Approach is as follows:\n1)We need to deeply understand what the problem statement is actually saying. For that we can use some examples to understand the problem.\n2)Then we need to find the best suited data structure for the given problem.\n3)Then draw the recursive tree for the problem to understand the approach.\n4)Side by side, write the algorithm for each step of the recursive tree.\n\n",2.5
-1485,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1486,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1487,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,For developing algorithms \n1. understand the problem statement \n2.find all possible way to solve that problem\n3.develop the algorithm which takes minimal time as well as the space complexities \n4.to check if the particular problem is optimizable.,1.0
-1488,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approach to develop algorithm :\n1. to write statements and step by step approach\n2. in form of pseudo code,1.0
-1489,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1) try to get the output .\n2) try to get best Time complexity and space complexity in optimization\n3)  first we check if the algorithm is able to handle Average case or not.\n,1.0
-1490,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,First we read and understand the problem given to us.\nThen we think about the most optimal approach to solve the problem.\nWe check its time and space complexity and see that if there is any approach which takes lesser time.\nWe finally code the approach we have made.\n,1.0
-1491,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"1. First, we need to look for an efficient approach to solve a solution i.e. whether to use backtracking, greedy or DP approach. \n2. Once, the approach has been decided, then the solution to the problem is implemented using the approach. For example, in dive and conquer, the problem is divided into smaller sub-problems and then we solve those sub-problems and then in return, the the main problem is also solved.\n3. After the algorithm is designed, we should try and check for the least time complexity possible. This will give an optimal solution.\n\n",2.5
-1492,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"1.Firstly we divide the problems into sub problems. \n2. Try to find an optimal and efficient solution to them individually using Backtracking, DP, Greedy and brute approaches.\n3. We try to implement the approach designed by us and compile our program with main function.\n4. After our algorithm is complete, we look for time complexity and analyze if any better way to modify the algorithm is possible.",2.5
-1493,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approach to develop an algorithm are:\n1) Analyze the given problem statement.\n2)Try to find the rough solution of the problem and try to create a flow chat for better understanding.\n3)write the pseudocode from the flowchart developed above.\n4)Check that the solution created by you is optimal and efficient or not if not try do develop other optimal solution for the given case.\n5)one optimal and efficient solution if found then that become the algorithm.,2.5
-1494,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Some approaches to develop algorithms are:\n1. Greedy approach: to consider the value or path that seems like best meeting our requirement without analyzing it further. May or may not give optimal solution. Eg: to find the shortest path to a goal, the next nearest city from the start is taken into our solution.\n2. Dynamic approach: to analyze all the possible solutions and then choosing the optimal one. Always gives optimal solution.",2.5
-1495,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"the approaches are firstly analyze the given problem then try to find the basic solution to that problem without going into the details at first then try to write the pseudo code for the solution according to you then dry run the code ,dry run the code for some of the random values then check whether you have got the desired or required result also consider the time complexity which is required to solve the given problem because half of the problem can be solved through that.",2.5
-1496,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,we divide the given problem in smaller problem and start writing code for smaller problem . with this approach we will start developing techniques for  bigger problem.,2.5
-1497,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1498,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,the approaches to develop algorithm is basically the basic logic applied in then question then develop the pseudo code required for the problem all the data structure to be used in that solution and then try to run the code with logic and data structure for all the test cases.,0.0
-1499,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1500,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Approaches to develop and algorithm are space complexity based or time complexity based. for lowest space or time complexity possible,",2.5
-1501,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,the approach to develop the algorithm is to take the input first then write base condition and then break main problem to sub-problems.,2.5
-1502,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop Algorithms are:\n1. First check that what kind of Data Structure can be use to solve the problem .\n2. Then develop the cases which can be use to solve the problem \n3. Find out that what kind of Time and Space complexity is perfect for the problem .\n4. Find out the base cases.\n5. Implement the searching and sorting operations if needed then apply the particular cases.\n6. Call the function.,2.5
-1503,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approached to develop algorithms are:\n1. First take input.\n2. divide the problem into sub problems.\n3. solve all the sub problems using same logic.\n4. Merge all the sub problems after solving them.\n5. Display the answer.,1.0
-1504,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,First the problem is analysed and the basic data structures and concepts are identified which will be definitely used. \nThen the logic is developed for which the lesser space and time is taken.\nfunctions are used and the final values returned.\n,1.0
-1505,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Firstly a brute force is thought upon the solution. It is the most basic solution which any problem can have, without worrying about any complexity.\nThe we try to break it in smaller problems and think solution for it and make recursive algorithm for the same.\nThen we analyze if there is repeatition of of any smaller problem and if yes then we store it as we won't call it again and simply use the stored value (Dynamic Programming - DP)",2.0
-1506,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Algorithms can be developed using Divide and conquer techniques. Greedy mechanisms, Dynamic Programming, backtracking concepts etc.",2.5
-1507,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First step should always be to understand the problem first and then start to look out for the data structures you  can use for the same and then accordingly calculating the time complexity of the approach. Always remember that the best algorithm is the one which is the fastest and covers all the use cases of the problem(be it base case, corner case, etc) ",2.5
-1508,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Our approach to develop an algorithm must be how to receive an optimal solution that also takes the minimum time and space.\nNow we can choose which one is more appropriate:\n-Divide and Conquer: To divide our problem in to subproblems then solve the subproblems and then eventually obtaining the required solution.\n-Backtracking- Exploring all the possible solutions of a problem and finding the right answer following our given constraints.\n-Greedy- Using a greedy approach to get a solution (to get more profit)\n-DP- To ensure optimal substructure and no overlapping problems.,2.5
-1509,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Algorithms can be developed on the basis of their efficiency. i.e, based on their time , based on their space complexity etc\nthat is through dynamic programming or greedy approach etc.",2.5
-1510,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approaches to develop am algorithm are:\n1. Divide the problem into sub-problems. \n2. Define the input. \n3. Check if you can directly use any of the existing algorithms.\n4. Modify the existing algorithms to solve the given problems.\n5.  Try to make the algorithms more optimized by using approaches of dynamic programming.,2.5
-1511,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"There are many approaches to develop Algorithms:\nSome of the most popular approaches are:\n1) Divide and Conquer:\nin this technique we divide the current problem in multiple subproblems such that each sub problem is exactly same as big problem.\neg: Rat in a maze, n-queens, uses backtracking that comes under Divide and conquer.\n2) Greedy Approach: In this approach we write algorithm in such a way that we get locally optimised result at each step.\neg: fractional Knapsack, Job scheduling etc algorithms uses this approach.\n3) Dynamic Programming: This is more optimised technique which aims at getting the optimised result at the end when program terminates.\nfor this this approach used memoisation and tabulation so that the repeated occurences can be ommited and value can be stored in a @-D array.\neg: 0/1 Knapsack, Longest Common Subsequence etc.\n",2.5
-1512,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop algorithms are:\nDivide and conquer - dividing the problems into smaller problems and getting the solution\nGreedy algorithm - Checking all the possible outcomes of a given problem\nDynamic Programming - Finding the most optimum solution to the given problem using memory memorization. \nBacktracking - Moving back and finding the the output to our solution.,2.5
-1513,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Firstly we need to write down the problem we need to solve, then we see if that problem can be divided into smaller sub problems, then we find a solution to solve that problem in the least time, then we implement that solution into a code.",2.5
-1514,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"To develop an algorithm first write the problem, then divide it into sub-problems (if possible), then solve the sub problems to find an optimized solution, then merge the solutions to find the final solution.",2.5
-1515,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"An algorithm is defined as the finite number of steps or sequence of operations required to solve a problem. To develop an algorithm we need to first check the user requirements, then design an algorithm as per the requirements. The algorithm should take a finite input and generate a finite output in the required amount of time complexity. It should be able to generate optimal solution to the problem in order to be efficient algorithm.",2.5
-1516,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1. Divide and Conquer: We solve small problems and handle base case so recursion will solve bigger problem.\n2. Backtracking: here we try all possible combination and pick the best one.\n3. Greedy Approach: here we try to pick best solution at every step.\n4. Dynamic Programming - Try all possible solution and save them and pick the best one.,2.5
-1517,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,here are three commonly used approaches to develop algorithms −\nGreedy Approach − finding solution by choosing next best option\nDivide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently\nDynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,2.5
-1518,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"each algorithm is developed in a different manner, but the main focus goes like analyzing the algorithm, implementing a brute force stratergy first , then trying out a more subtle approach with much less time complexity considering various methods of algorithm forming like backtracking, divide and conquer, greedy approach or a dynamic approach and finally seeing if there is any other method to optimize the problem further.",2.5
-1519,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,the first approach is thinking a brute force approach to solve a problem. then optimizing the algorithm using various data structures and making changes in the approach to solve complex problem efficiently,2.5
-1520,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"there are many common approaches such as brute force , greedy and dynamic programming to develop algorithms. Brute force is the simplest way in which apply any possible simple logic to solve the problem at hand. IN greedy approach we try to take the best possible root at each step which increases the possibility of getting the best result. Dynamic programming always ensures the most optimal solution in all cases , as in this we go through all possible outcomes and choose the best among them.",2.5
-1521,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,first we look closely upon the problem statement and briefly understand the things asked\nthereafter we use different approached like divide and conquer or greedy or backtracking and many more to obtain the solution\nafter which the space and time complexities are compared to get the optimized solution of the given problem,2.5
-1522,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approaches to develop algorithms: -\n1. Algorithm must give complete solution to a problem. \n2. The solution may or may not be optimal.\n3. Less Time consuming ,2.5
-1523,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"1)Waterfall approach\nDeveloping algorithms include understanding the problem, taking into consideration the space and time complexity, checking the feasibility of the problem, whether the algorithm is complete and optimal",2.5
-1524,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"1)divide and conquer : we divide the problems in subproblems\n2)greedy : the first approach which hit mind first , it does not  provide optimal solution\n3)dynamic programming : using recurssion we divide problem into subproblems which provide optimal solution",2.5
-1525,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"we analyze our requirements , and find the best algo for the problem which takes the least time and uses least amount of resources",2.5
-1526,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,\nAnalyze data\nObjectify need \nTime analysis \nSpace analysis\n,2.5
-1527,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1.first understand the problem properly\n2.apply the brute force approach \n3. Try to reduce the time and  complexity and optimize it \n4. If its work that particular set of information then algorithm developed ,2.5
-1528,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"to approach developing an algorithm \n1. we need to read and understand the problem carefully\n2. apply brute force for to problem first\n3. now see how we can optimize the solution \nwe can use different techniques like divide and conquer, backtracking, greedy etc.",2.5
-1529,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"recursive , divide and conquer, greedy, dynamic",2.5
-1530,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop algorithms is coming up with a pattern with the knowledge of DS and attempting to use minimum space and complexity in order to form the most optimum algorithm ,2.5
-1531,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approach to develop algorithm is based on its efficiency to find the solution in minimum time while taking minimum space in memory of our system,2.0
-1532,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches are to the develop of the algortihm:\nGREEDY APPROACH\nDYNAMIC APPROACH\nDEVIDE AND CONQUER APPROACH,2.5
-1533,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,we used first find all the possible ways and then check the complexity for the program so it can run in the least time possible ,2.5
-1534,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"There are divide and conquer which divides problem into smaller problems. Backtracking algorithms follow a particular way and if a solution is not found, backtracks and goes to another way.",2.5
-1535,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1536,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"the approach these day is to find the fastest way to search, sort, iterate through the data, for fast reponse time.",2.5
-1537,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1538,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"the approaches to develop algorithms are firstly, we understand the problem and try to devise various ways using different test cases \nthen we implement a simple code using the algorithm that we've devised and then we workout on the pre designed algorithm and implement the one which is best suited with respect to our problems.",2.5
-1539,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Master's theorem,2.5
-1540,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Following are the approaches to develop algorithms.\n->Greedy algo: Taking descision at every step\n->Dynamic Programming: Taking out best/efficient possible answer by framing a formula or method.,2.5
-1541,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,An algorithm is developed depending on its use. Most algorithms are used to find the optimal result of of the problem like the search algorithms. They are very helpful in various fields  and studies.,2.5
-1542,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,the approaches to develop the algorithm is finding the optimum and efficient solution of the problem ,2.5
-1543,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1544,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"the algorithm should be time and space efficient. this means that the program shouldnt contain any unnecessary loops. predefined data structures take up a lot of space as well, so its better to take the input dynamically so that no space is wasted at all \n",1.0
-1545,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,we develop algorithm so that to get get the good or optimal approach for any problerm,1.0
-1546,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Try to understand what the problem wants you to do\nFind a data structure for the given problem\nTry solving the problem with brute force\nTry to make your solution better by making efficient algorithms in terms of time and space complexity,2.5
-1547,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,greedy method:finds local optimal solution\ndynamic:stores the solution of the sub problem in dp[][] to provide efficiency by decreasing time\ndivide and conquer:divides the problem into sub problem until it cant be divided further and then solves each sub problem,2.5
-1548,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1549,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,to develop algorithm take a variable and develoop such that it takes the minimum time less space,1.0
-1550,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,. write the pseudo code \n. do dry run,1.0
-1551,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,define a solution to a problem\ncheck if the approach to the problem gives reliable and consistent solution,1.0
-1552,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1553,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1554,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approach to develop Algorithm:\n,2.5
-1555,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approach to develop algorithms is to by building logic to reach the goal in given time and space complexity.,2.5
-1556,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,While developing algorithms we take care of the time and space complexities and write a program which implements the logic satisfying these conditions,2.5
-1557,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop algorithm is to make a certain code to takes less space and take less time to works.,2.5
-1558,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,development of algorithm can be done by first thinking of all possible cases and then finding some base case which will have a surely  solutions to them without the help of other functions,1.0
-1559,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,first we analyze and  write a pseudo code that efficiently solving the problem in terms of time and space .we find this complexities with the help of recurrence functions and then we implement the algorithm.\nThese are the steps:\nAnalyzing and thinking a solution\nsolving (pseudo code)\nimplementation \n,1.0
-1560,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The algorithm should must return a feasible output.\n,1.0
-1561,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1562,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1563,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,There are several approaches like divide and conquer in which first we divide the problem in subpart and then merge them,2.5
-1564,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First try the brute force approach to solve a problem. Then try try to optimize the solution by making use of different techniques like divide and conquer and greedy approach. We can use recursion.  If we can see the there is scope for improving the space complexity and time complexity namely to reduce the number of repeated operations, we can apply dynamic programming. ",2.5
-1565,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"approaches can be to know how much work it does, to get the need of it\nto use the current data structure at correct place.\nto know the edge cases involved in developing algorithms\nto keep in mind about the complexity \n",2.0
-1566,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,To Develop Algorithms we need to\n->Firstly Understand Ques Correctly\n->Develop the logic \n->Then think which Data Structure is most Suitable\n->then try to optimized the algorithm in terms of time and memory\n->Most Importantly our algorithms must have correct output of all test cases and edges test cases,1.0
-1567,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"divide and conquer approach , brute force approach ,FIFO , LIFO etc.",1.0
-1568,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approaches to develop algorithm are mainly the devide and step techniques. Divide the given problem into a set of subproblems. Solve every problem individualy recursively.Combine the solution of subproblems into a solution of the whole problem.,2.0
-1569,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"by developing recurrence relations \nusing specific time complexities space complexities, keeping the record of its worst case best case we can develop algorithm.. which will work accordingly",1.0
-1570,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,
-1571,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Analyze the given question \nlist out the memory blocks to be used for solving the problem\nimplement solving technique using your code, for example - while using backtracking to solve N queen problems, recursive tree is drawn, \nbased upon the logic try to write a pseudo code",2.0
-1572,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,To develop an algorithm we first need to know where that algorithm will fail and where will it apply.,1.0
-1573,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approaches are to first understand the problem statement and then write down the pseudo code briefly and then also analyze the edge cases and handle them. ,2.0
-1574,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,First we should analyze the algo thoroughly and then optimize it according to the best space and time complexity found.,2.0
-1575,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"first of all we need to identify what the problem is. We should then make a pictorial representation of the data structure used and the whole working of how we want to approach the problem on paper. Then we should start writing the code. As far as approach is concerned, observe which data structure can be used to solve the problem in most effective way. Then using our prior knowledge about the data structure, we must either apply the classic approaches, and if we find a better approach, we should apply that.",2.0
-1576,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approach includes-\nunderstand the algorithm\nanalyse it\nbreak down  the algorithm\nwrite the pseudocode or flowcharts,1.0
-1577,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"With context to the problem, we have multiple ways of working out the solution. But we have to choose our approach in a very sensible manner while taking care of the optimization and the test cases. For example, in knapsack problem. DP and Greedy both can be used to derive the answer. But 0/1 problem becomes a problematic approach if we choose greedy. ",1.0
-1578,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,We first think of a brute force approach that can solve the porblem and calculate its complexity and then we try to optimise the problem using an \,2.0
-1579,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Basic approaches remain the same for all the algorithms where it is divide and conquer, backtracking ,etc. \n1. Try to simplify the problem into simpler cases. \n2. Try to find out data structures that can be used to solve the cases. \n3. Look for validity using test cases. \n4. Further optimize it and look for better ways/ data structure.\n\nDivide and conquer -> divide problem into smaller subproblems and find solution of each of the smaller subproblems. Then recombine these to solve the bigger problem,\n\nDP - we solve each of the cases and store all the solutions to smaller problems in matrices or arrays to cut out the overlapping subproblems of the recursion.\n\nBacktracking - > Go in all the directions to find the solutions (except those which are cut short because of invalid advancement. for example - in rat in a maze problem, we try all the paths to reach the finish block but we don't in the directions blocked by the obstacles. ",2.0
-1580,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Main approaches are:\nGreedy method\nDynamic Programming\nBack Tracking \nLinear method \n,1.0
-1581,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Brute force approach: To try to develop an algorithm without using any pattern or pre requesite \nGreedy approach: This approach solves the problem step by step so that the problem occurring first is solved first\nDivide and conquer approach: This algorithm divides the problem into set of sub problems and each is solved individually,1.0
-1582,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Data structures can be used for collectively  storing various data types together for solving a particular question.,2.0
-1583,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Step 1. Implement a bruteforce solution\nStep 2. Try to reduce the time and space complexity \nStep 3. Apply asymtotic analysis and various data structures that can reduce the time and space complexity of the algorithm.,2.0
-1584,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,There are three commonly used approaches to develop algorithms −\nGreedy Approach − finding solution by choosing next best option\nDivide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently\nDynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1.0
-1585,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First, we read the problem and then we think how to solve it, not efficiently but only a brute force method to solve. Then step by step, we try to reduce time complexity and reach an efficient solution. For example - Like while solving backtracking questions, we first think about the problem, then apply brute force solution. After that, we try to apply recursion and solve more efficiently.",2.0
-1586,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"an algorithm of a problem can be developed by divide and conquer, backtracking. greedy or dynamic programming approaches.",1.0
-1587,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Greedy\nBacktracking\nDivide and conquer\nDP\n,2.0
-1588,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"To develop an algorithm, understand the problem, try and build a solution which uses less time even on larger inputs and also does not take a lot of space.",1.0
-1589,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"first understand the problem, second break it into sub problems and achievable steps, third brought down a way of achieving that sub problem, fourth check is there another way!, if yes then find which one is more efficient use that, if no move further to next step, fifth write the algorithm in the form of code for the problem and test if it works properly.",1.0
-1590,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1. Understand the problem statement.\n2. Try to find the best possible approach to find the solution of that problem.\n3. Use the analogy of real life solutions in computing terms.\n4. Design the algorithm keeping all these things in mind. \n5. Try to minimize the space and time complexity of the designed algorithm.,2.0
-1591,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Divide and conquer -  First the data sstructure is divided until the given condition and once it is fulfilled , it is operated upon.\nBranch and bound - \nGreedy approach - Problem is solved by sorting the data structure in a particular order and then finding the max , min or the data fulfilling the specidied condition\nDynamic programming - Reducing the time and space complexity by storing the values processed in the recursive stack space in an array so that the function need not process the value for the same input when encountered and can access it from the array.\n",2.0
-1592,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"while developing an algorithm we first write our requirement and the information given to us then we decide which approach to use to create the algorithm like divide and conquer, greedy method, dynamic programming & backtracking. after that we write the pseudo code for the algorithm which is basically breaking the problem into different steps like making a counter, running a loop. etc. and then after that code is written for the algorithm in a particular language.",1.0
-1593,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,To develop an algorithms we need to understand the requirement of the algorithm and develop a pseudo code and check it for different test case and specially find the base case where algorithm may act differently. Moreover take care of time complexity and space complexity better the time and space complexity better the algorithm.  ,1.0
-1594,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop algorithms :-\n1) Identify problem\n2) Identify data structure to use and their working\n3) Implement the algo\n4) Try to make it more efficient,1.0
-1595,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Common Operations performed on data-structures are-\nSearching\nSorting\nInsertion\nDeletion,1.0
-1596,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,for developing the algorithms first you should have to make approach for solving the problem as ,2.0
-1597,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,
-1598,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"greedy approach, ",1.0
-1599,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,we have to follow some rules before developing an algorithm first is that algorithm following the rules that are given or not and second is to save the time and space according to the question,2.0
-1600,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First we analyse the problem at our hand, and try to find out the best solving technique be it backtracking, divide and conquer, or dynamic programming. We can also use brute force to get a clear idea of the problem and get a vanilla algorithm and then work on it.  After designing an algorithm there are 2 ways: prerioiri and postreori to find the time complexity of the algorithm, after deducing it we see if it has scope for improvement, if yes then do so.",1.0
-1601,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,we wil use following approaches to develop algorithm :-\n1) if the algorithm is complete or not.\n2) check its time and spaces complexity (means they should be minimum).\n3) check for admissable means if the solution given by the algorithm is optimal or not.\n\nbased on these approaches we will develop the algorithm,1.0
-1602,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop an algorithms are\n*)understanding the problem statement .\n*)understand the asymptotic notations such as desired time complexity and space complexity.,1.0
-1603,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1604,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,by writing pseudocode,2.0
-1605,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"first try to connect the problem with real world scenario , then develop a raw solution to the problem ,once it is able to handle all the possible test cases try to optimize it by minimizing  its time and space complexity.",1.0
-1606,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,to develop algorithms  following approaches are taken-\n,1.0
-1607,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approach to develop algorithms is to \nfirst understand the problem \nfind put the  ,1.0
-1608,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"The approaches to develop algorithms are based on the nature of the problem i.e. if the problem can be divided into smaller problem of similar nature, what kind of data structure we require to efficiently solve the problem. Based on these techniques there are various approaches like divide and conquer, brute force, dynamic programming, greedy approaches, etc.",1.0
-1609,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1.Divide problems into set of subproblems\n2.Solve every subproblem individually\n3.Combine the solutions to obtain solution for the original problem,2.0
-1610,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"To develop the algorithms, we simple require an overview of what we are going to do to solve the problem and the general steps which we are performing one by one to approach the solution.",1.0
-1611,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1. analyze the problem clearly.\n2. break the problem into sub problem if its too large.\n3. design the algorithm for the sub problem if possible.\n4. Analyze the complexity.\n5.Dry run for various inputs and try to optimize it.,2.0
-1612,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Algorithms are written in order to find the most efficient way ,precisely saying the way having least time complexity. Approaches to algorithm include firstly to find out any way through which we can get to solution of our problem. Secondly to find any other way involving less time, Thirdly to analyse if its the best possible way out. Fourthly to code it.",1.0
-1613,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approaches to a developing an algorithm are:\nrecursive\niterative\nsubstitution\n,2.0
-1614,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First, analyze the problem thoroughly. Then write down the basic , common ideas or the steps to achieve that algorithm somewhere and when the brute force gets ready, then look upon the time complexity and after that on the space complexity.",1.0
-1615,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Basically, algorithm is  a collection of steps that tells about how a particular task is happening through code. ",1.0
-1616,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1617,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approaches to develop an algorithm are \nbrute force approach\ngreedy approach\ndynamic programming\ndivide and conquer,1.0
-1618,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"An algorithm is a set of generalized steps which are obeyed to write computer programs by which we can solve a certain problem.\nThe approaches to develop an algorithm is-\nFirstly clearly under stand the problem statement and understand the edge cases and all the constraints.\nNow, we will be in need to find the best data structure to be used to code out that particular algorithm.\nTry to solve the problem on the bases of brute force approach and then try to do optimization for the solution.\nTry to use the properties of existing algorithm and then invent new algorihtm by writing the steps.",1.0
-1619,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Greedy Approach - we make optimal decisions for present case to get a optimal solution for case\ndynamic approach - we store the data used in case it is needed in future\nbacktracking - in this we backtrack to previous case if answer not found ,1.0
-1620,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"The main purpose of developing an algorithm is to keep in mind it must work in every language.\nTo develop an algorithm we must look at the problem in a simpler manner and develop an efficient code which can help us achieve our solution\nWhich can be obtained by implementing any of the following strategies: Divide and Conquer, Greedy approach, Backtracking and Dynamic programming",1.0
-1621,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"algorithm is a stepwise solution to a problem. To develop an algorithm first we will try to think about the most naive approach, then break it into subproblems and try to improve it in a such a way that it become more cost efficient  and time complexity and space complexity is better.",1.0
-1622,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,the approaches to develop algorithm are to find the time complexity and space complexity.,1.0
-1623,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,First we use a brute force approach and check whether we can solve the question by it in the best time and space complexity. Then if we can get a better time complexity than the original brute force approach then we try to make the algorithm more efficient by checking whether the question given to us can be solved using any other approach. Then we make the algorithm more efficient by changing its space and time complexity.\n,2.0
-1624,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"To develop an algorithm is to solve a smaller problem, which in turn solves a bigger problem. like. taking example of a Fibonacci series try to solve the first to two or three answers and accordingly frame a formula or algo and hence apply it to the bigger problem.",2.0
-1625,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,some common approaches for developing algrothm are \n1. dynamic programing\n2. backtracking\n3greedy approach\n4. divide and conquer,2.0
-1626,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approach to develop algorithm is first we need to understand the problem. Then think of brute force solution. See the time and space complexities and how we can improve those. See if existing algorithms or methods exist and how and if using those algorithms or modifying them we can solve the problem. the pseudo code is written and seen if it is better than previous approaches and then finally converted to code. ,1.0
-1627,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Divide and conquer approach - the problem is divided into smaller subsets and then solved\nBacktracking approach - all the possible solutions of a problem are traversed to get the optimum result\nGreedy approach-the first optimum solution is taken into consideration\nDynamic Programming approach-tabular solution for the optimum solution,1.0
-1628,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1) analysis of the problem at hand\n2) writing the approach and pseudo code\n3) finding the best solution for the given problem\n4)adjusting the solution according to the problem\n5) debugging the solution,1.0
-1629,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1630,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Firstly, the given problem is analyzed and then the problem is divided for further analysis into subproblem\nThen, the best solution for every subproblem is analyzed and then the solution of the subproblems are adjusted according to main problem \nMost Efficient method found in this process is used at the last",1.0
-1631,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approaches to develop algorithms:\nfirst we develop the brute force of the algorithm working in the question\nthen we find out if any better approach with better time complexity and space complexity could we used and then we apply that approach to solve our problem.,1.0
-1632,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,the approach to develop algorithms is that they should take the least time complexity and space complexity when solving a problem ,1.0
-1633,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop algorithm are:\na)Firstly we see what the question is asking\nb)We ,1.0
-1634,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approaches to develop the algorithms are:-\n,1.0
-1635,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1636,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1637,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approaches to develope an algorithm are:\n1)divied and conquer\n2)backtracking\n3)greedy algorithm\n4)dynamic programming\n5)branch and bound algorithm,1.0
-1638,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,to develop and algorithm you must understand the problem statement thoroughly and its requirements. After that you must identify the time and space complexity of you approach and them improve it to the better and best algorithm possible,1.0
-1639,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1640,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"We try to find a solution to a problem such that it is also efficient. We can use greedy method, dynaminc programming, divide and conquer, backtracking etc.. to do so",1.0
-1641,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1642,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approach to develop an algorithm is to first analyze the logic behind the outcome and then develop the particular algorithm behind it.,1.0
-1643,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,steps to develop an algorithms are:\n1. understand the problem in your mind and perform a dry run on pen and paper\n2. think accordingly which data structure to use\n3. form your algo according to the knowledge you get from dry run.,1.0
-1644,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1645,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"The task of user is to find the best possible approach to develop the algorithm so that it has the minimum time complexity and it covers all the possible test cases so that the program does not fail for any of the hidden base case. There are many approaches such as 1) greedy approach, 2)dynamic approach,  3) divide and conquer approach, 4) brute force approach.\n",1.0
-1646,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,algorithms are develop according to better time and space complexity.,1.0
-1647,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Greedy Approach − finding solution by choosing next best option\nDivide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently\nDynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1.0
-1648,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1.analyze the problem\n2.check what do we have\n3.think how can we solve the problem with the data given\n4.come up with solution,1.0
-1649,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First find the best suitable approaches that may be applied on given theorem such as greedy , dp etc.\nthen find the suitable data structure that ease to traverse the data \nand then build the algo step by step",1.0
-1650,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1651,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"Linear data structure, is when traversing goes in a single line ex-array, linked list, stack",1.0
-1652,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1)Brute-force or exhaustive search.\n2)Divide and Conquer.\n3)Greedy Algorithms.\n4)Dynamic Programming.\n5)Branch and Bound Algorithm.\n6)Randomized Algorithm.\n7)Backtracking.\n,1.0
-1653,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1. Understand the problem statement.\n2. Try to devise steps that can lead to the solution.\n3. Write those steps in simple language to make the understanding easier for the reader.\n4. Include all the necessary information that leads to the solution.,1.0
-1654,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1.) Divide and conquer approach where we work on a sub problem and keep on adding the solution  to get the solution of the big problem.\n2) Backtracking approach which is used to find all the solutions of a problem whether it is optimal or not .\n3) Greedy Approach where we find the best possible solution at each step.\n4) Dynamic Approach,1.0
-1655,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,To develop a algorithm:\n1.We firstly generate the problem statement;\n2.A problem solution is proposed;\n3.Asympotmatic analysis;\n4.Testing;\n\n,1.0
-1656,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"The approaches to develop algorithms include first breaking down the problem statement,then choosing the best suited data structure or data structures as per the requirement \nand writing the solution in a simple understandable language.",1.0
-1657,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1. Analyze the problem and generate a problem statement.\n2. Think about the possible ways to solve the problem.\n3.Write the optimize algorithm according to your knowledge.,1.0
-1658,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1659,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"approaches to develop algorithm are-;\n1-check its time complexity, analysis of best case, worst case all types of scenarios\n2-check its space complexity how much space it would be requiring and optimise it to use least space as more space can exhaust large amount of memory of system\n",1.0
-1660,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1661,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First the problem is studied thoroughly and best solutions are written. Then using backtracking , divide and conquer techniques an optimal solution is found. In some cases values of past problems are stored to refer to incase the program faces the problem again , like in dynamic programming.",1.0
-1662,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,algorithms should have optimal time complexity and space complexity and it should give the optimal solution .   the algorithms should be efficient,1.0
-1663,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop algorithms include the motive of finding the optimal solution to a problem i.e. the solution which is both time-efficient and space efficient.,1.0
-1664,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"there are various approaches to develop an algorithm ,one such is known as brute force or naive approcah,having absurd time and space complexity.After which we try to optimise our solution by using a suitabledata structure and a suitable algorithm ,thereby providing both a time and space efficient solution.",1.5
-1665,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"the first approach to develop algorithm is to reach the solution, which can be done by making the problem smaller and smaller and finding its solution. then find ways to optimize that solution in the best way possible way. time complexity should be kept in mind",1.5
-1666,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"First we have to identify the problem after that we have to find algorithms which may solve the problem ,after that we have to find space and time complexty and chose the most effecient one .",1.5
-1667,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,I have first approach is that the algo. is not be large and main approach is that less time complexity in comparing to other method . For developing the algorithm we know the appropriate logic to attempting and solving the problem.,1.5
-1668,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"first we find a solution to the given problem, then we find ways to optimize our solution. divide and conquer, greedy approaches are some examples of algorithms.",2.5
-1669,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"The first approach is to apply brute force, that is the process without thinking about the time complexity. Then we can try to optimize the algorithm aiming to reduce the time complexity.  We should always consider the worst case scenario while writing any algorithm and take further step accordingly.",2.0
-1670,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,divide and conquer approach\nback and bound approach\nbacktracking approach\ngreedy approach\ndynamic programing,2.5
-1671,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Objective of developing an algorithm is offering the user with the most time efficient as well as with the space efficient solution to his problem .The solution must perform best amongst the best and worst case scenarios.,1.5
-1672,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,First we should read the question and find which Data Structure is being used in that particular program and to find that data structure we have to know what operations has to be used to solve that problem then make a flowchart and write the program acording to it like if in a question it is asked for a function that performs last in first out then we have to use stack in it  ,1.0
-1673,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,to develop a algorithm first we need to check the problem and than we need to think how efficiently and producitvely we can solve the problem for that we have to divide the problem in parts and than solve each part to get the final solution by ths we can develop a algorithm\nexample dyanamic programming \ngreedy approach \ndivide and conquer,2.5
-1674,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,An algorithm should have least time and space complexity. we constantly try to make algorithms that have lesser time complexity.,1.5
-1675,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,There are multiple approaches to develop an algorithm like by optimsing the time complexity of algorithm and optimising the space somplexity of the algorithm.,1.5
-1676,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,first we analyse the problem and then approach towards the solution,1.0
-1677,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,first we can use brute force approach and then from there work on reducing its complexities,1.5
-1678,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Approaches to develop algorithm can include identification of the basic mechanism involved in the problem following to this will be the time complexity efficient algorithm that would be given priority in selection of algorithms,1.5
-1679,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1680,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1681,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"1. Divide and conquer approach: Divide the problem into subproblems . Reach the smallest subproblem recursively . Write solution for smallest subproblem. Backtrack to reach final answer.\n2. Recursion and backtracking: use recursuion to trace all solutions . If you find an invalid path , backtrack.\n3. Greedy approch: At each step, write the best fit solution for that time.",2.5
-1682,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"approaches TO develop algorithms;\n1.using recurrence relation ->we obtain the recurrence relation \n2, guess approach -> we guess and then obtain time complexity\n",2.0
-1683,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1684,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1) read the problem statement \n2) think of the similar examples\n3) think of a particular solution to it \n4) develop some possible test cases and run them roughly\n5) find the more efficient solution once you have find the basic approach,1.0
-1685,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1686,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,First we have to analyse the problem and then we have to identify suitable type of data structure that could be used to solve the problem.,0.0
-1687,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,concept of notation which helps in develop algorithm,0.0
-1688,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,to develop the algorithm understand the problem statement break it into task perform each task separately optimism the algorithm  ,1.5
-1689,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,Algorithms can be developed through several approaches wheather it be time approach or space approach. ,1.0
-1690,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,first we have to understand the problem then we have to find a perfect algorithm,1.0
-1691,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1692,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1) we should understand the need of  problem\n2) build the logic  find its is correct approach or not\n3) find the time complexity of developed algorithm,1.0
-1693,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"the one an only approach to make algorithm is by using greedy approach in which we are getting more profit and less usage of space , also reduces time complexity",1.0
-1694,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1. Problem Statement\n2. Sources available\n3. Time efficiency\n4. Space efficiency,1.0
-1695,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,to develop algorithm we first analyse the code and check for its time and space comlexity and find ways in which we can edit the code so that we have lower time and space complexity and our output is close to accurate.,1.0
-1696,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"to develop algorithm firstly one should know how to approach to the problem, what are the mandatory conditions  required for the problem to solve, then write each steps in normal english form mentioning the mandatory conditions",1.0
-1697,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,to develop an algorithm first we will understand the problem \nthen we will try to find the method or approach to solve the problem\nafter finding the appropriate approach we will create the steps to execute the approach which we call as algorithm,1.0
-1698,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,There are two approaches to develop algorithms:\n\nPriori:\nDoes not depend on machine\nDo analysis before implementing the algorithm \n\nPostriori:\nDepends on machine\nDo analysis after implementing the algorithm ,1.5
-1699,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,The approaches to develop algorithms are understanding the problem statement for which we have to develop the algorithm then divide the problem into subproblems and find approach to solve the subproblems ,2.0
-1700,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,need to solve the problem\ntechnique which we will apply to solve it\nability to compare the problem with real word experiences.\n,1.0
-1701,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,,0.0
-1702,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,"first the logical algorithm is written \nthen it is converted to pseudo code\nthen into languages like c++,java etc.",0.0
-1703,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,for building a certain algo we need to address the problem and look forward to that with the possible nearest solution by which we can get the our desirable output and we meet to build algo by take computation power and the time complexity in the mind .,1.0
-1704,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,1getting the right approch the probelem using basic ,0.0
-1705,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,it  depends of need  ex greedy approaches dynamic approaches for optimization's,2.0
-1706,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,greedy.(is the use of maximum optimization).,1.0
-1707,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,na,0.0
-1708,Briefly explain the approaches to develop algorithms.,There are three commonly used approaches to develop algorithms –(1) Greedy Approach − finding solution by choosing next best option (2) Divide and Conquer − diving the problem to a minimum possible sub-problem and solving them independently (3) Dynamic Programming − diving the problem to a minimum possible sub-problem and solving them combinedly,approach to develop algorithms:\n1. find the time complexity \n2..correct approach for the question\n3. result should be minimal,1.0
-1709,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Longest Common Subsequence, Djikstra Algorithm,Recursion, FLoyd Warshall Algorithm, Hamiltonian Path, Knapsack.",1.0
-1710,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points)," examples of divide and conquer algorithms are genome sequencing, dictonary searching etc",1.0
-1711,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),some examples of divide and conquer algorithms are merge sort and quick sort in which we divide the problem into smaller problem and then solve them,1.0
-1712,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1) Merge Sort\n2) Binary Search\n3) Quick Sort\n4) Shortest distance between points in a Graph,2.5
-1713,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort\nQuick Sort\nPairwise shortest distance between two points,2.5
-1714,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"There are some examples of divide and conquer algorithms like Merge Sort ,Quick sort",2.5
-1715,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples of divide and conquer are binary searches and merge sort . \n,2.5
-1716,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),. quicksort.\n. mergesort\n. binary search,2.5
-1717,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples of divide and conquer algorithm are \n- Merge sort\n- Binary Search\n- Quicksort,2.5
-1718,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1. quick sort\n2. merge sort\n3. Strassen's algorithm,2.5
-1719,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer are as follows-\n1. Closest pair problem\n2. Stock pricing problem\n3. Strassen multiplication problem\n4.Merge sort \n5.Quick sort,2.5
-1720,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1)Merge sort \n2)Quick sort  \n3)closet pair path problem,2.5
-1721,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Quick sort, merge sort, binary search",2.5
-1722,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge and Quick Sort,2.5
-1723,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples of divide and conquer algorithms are :\n- quick sort,2.5
-1724,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points), Merge Sort\n- Matrix Multiplication.\n,2.0
-1725,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),divide and conquer algorithms are best used when the subproblems of a problem are similar to the original problem and same approach can be used to solve all the sub-problems and lead us to desired output\n\nSome examples are: \n1. Merge Sort\n2. Matrix Multiplication,2.0
-1726,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),,0.0
-1727,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, quick sort, median search, binary search",2.5
-1728,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Examples are:\nBinary Search\nMerge Sort\n\n,2.0
-1729,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, Quick sort",2.0
-1730,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"insertion sort , quick sort are the very common examples of divide and conquer algorithms.",2.0
-1731,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some Examples of divide and conquer algorithms are- Binary search,merge sort,bubble sort,selection sort,insertion sort and median search ",2.5
-1732,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Divide and conquer algorithms are used in solving many problems (like to find minimum distance between two points in a plane, etc ) . The concept of this algorithm is to divide the main problem into smaller subproblems. Then we find solution of these smaller subproblems and subsequently compute the solution of main, big problem. The main catch in this algorithm is that the smaller subproblems should be similar to the parent problem, otherwise this algorithm will not work. \nSome examples of Divide and Conquer algorithms are : merge sort, quicksort,  etc.  ",2.5
-1733,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort ,quick sort etc. are the example of divide and conquer as in divide nd conquer we divide the problem into subproblem and then  recursively  get the resultant output by combining the output got from subproblems.",2.5
-1734,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort: In which we keep dividing the arrays into sub arrays until only one element is present in each sub array. Then we merge those sub-arrays into a sorted array.\nQuick Sort: The pivot divides the array in 2 parts one side sorted and one side unsorted. the pivot keeps on changing as the array gets sorted.\nBinary Search: We keep dividing the sorted array into halves until we find the element.\n,2.5
-1735,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Binary search, quick sort and merge sort.",2.5
-1736,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Binary search, quicksort and merge sort.",2.5
-1737,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some example of divide and conquer algorithm are :\n1) merge sort\n2)quick sort\n3)binary search\n\n\n,2.5
-1738,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer algorithms are:\n1. Merge sort\n2. Closest points,2.5
-1739,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"some common examples for divide and conquer algorithm are merge sort, quick sort, binary search, radix sort.",2.5
-1740,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),graph coloring\n\nHamiltonian cycle\n,1.0
-1741,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"some common examples of divide and conquer algorithms are merge sort, quick sort ,etc.",2.5
-1742,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),some examples of divide and conquer are-1->merge sort 2->radix sort 3->binary search 4->quick sort.,2.5
-1743,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some Examples of divide and conquer algorithms are: \nMerge sort, Quick sort, binary search, etc. ",2.5
-1744,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer are Binary search, Merge Sort and Quick sort.",2.5
-1745,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples are:\nquick sort , binary search , merge sort",2.5
-1746,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer algorithms are : Merge and Quick Sort.,2.5
-1747,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer are:\n1. Merge Sort.\n2. Quick Sort.\n3. Binary Search.,2.5
-1748,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"divide and conquer algorithms include binary search, finding subsets, merge sort, quick sort, etc.",2.5
-1749,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Binary search\nMerge Sort,2.5
-1750,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"some examples of divide and conquer Algorithms include median search, quicksort, merge sort ,binary search etc. Here they divide the problem into further smaller sub problems and recurs to find perform the desired operation.",2.5
-1751,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"in divide and conquer algorithm you divide the problem into multiple small problems and then accordingly try to find the desirable solution in all the small problems. \nImagine you have to search for a particular value in a given array of input by the user, it will be very time consuming if you do it by brute force approach it is better to divide the array into small piece and then run the search for it. IT WILL SAVE TIME!",2.5
-1752,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Divide and Conquer algorithms are to divide the bigger problem into smaller subproblems, then solving the subproblems and eventually obtaining the result of the original problem.\nThe best example of this algorithm is - Merge Sort.\nIn Merge Sort, the array is divided into subarrays till one element is left in each. Then it is sorted then the subarrays are merged and again sorted until the entire original array is sorted.",2.5
-1753,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Various searching and sorting techniques use the divide and conquer approach like normal search , binary sorting, insertion sort, selection sort etc",2.5
-1754,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples of divide and conquer are:\nBinary search algorithm, quick-sort, merge sort, N-queens problem, etc.",2.5
-1755,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"example of Divide And Conquer algorithm are:\nQuicksort\nMerge Sort\nBinary Search\nN-Queens,\nRat in a MAze",2.5
-1756,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Examples of divide and conquer algorithms are:\nN-knights\nRat in a maze\nN-Queens,1.0
-1757,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Binary search, merge sort, etc.",2.5
-1758,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples of Divide and conquer algorithm are- merge sort, binary search.",2.5
-1759,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Various sorting and searching techniques are based on divide and conquer algorithm like merge sort, binary search. Binary tree traversal also comes under divide and conquer algorithm.",2.5
-1760,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Examples:\n1. Merge Sort\n2. Quick Sort\n3. Binary Search,2.5
-1761,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge Sort, Quick Sort",2.0
-1762,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, quick sort, etc.",2.0
-1763,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"It can be used in merge sort , quick sort and other algorithms that require breaking the problem into smaller pieces, solving them and then recombine",0.0
-1764,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, quick sort ",2.0
-1765,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\nquick sort\nradix sort and many more,2.5
-1766,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples to divide and conquer algorithms - merge sort, quick sort, binary search ",2.5
-1767,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, \nQuick sort\nBinary search\n",2.5
-1768,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1)Fibonacci series\n,1.0
-1769,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort , quick sort",2.5
-1770,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Quick Sort\nBinary Search\nMerge sort,2.5
-1771,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Binary search ,1.5
-1772,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples of divide and conquer algorithms:\n1. merge sort\n2. binary search,2.5
-1773,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples are merge sort, quick sort, binary search.",2.5
-1774,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),There are multiple examples of divide and conquer few of which are \n1. merge sort ( splits the given data further and further while swapping)\n2. binary search ,2.5
-1775,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Divide and conquer algorithm examples are merge sort, quick sort and closest-pair algorithm",2.5
-1776,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),EXAMPLES OF THE CONQUER ALGOROTHMS ARE :_\nMERGE SORT\nQUICK SORT\nSELECTION SORT\nINSERTION SORT\n,2.5
-1777,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"quick sort ,merge sort ,binary search",2.5
-1778,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer algorithms are: \n1. Merge Sort\n2. Binary Search,2.5
-1779,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Quick sort, Binary search, Merge sort are some examples of divide and conquer algorithms.",2.5
-1780,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, quick sort.",2.5
-1781,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1.Binary Search\n2. Merge sort\n3. Quick sort,2.5
-1782,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"divide and conquer algorithm works on the basis that we divide the problem into set of small problems and then solve them at an individual level and later compile them together to define the final solution of the problem.\neg-Merge sort, insertion sort, selection sort.",2.5
-1783,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),quick sort\nmerge sort,2.5
-1784,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Following are  some examples of divide and conquer algorithms:\n->Merge sort\n->Quick sort\n->Binary search,2.5
-1785,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, quick sort are some of the examples of divide and conquer algorithms.",2.5
-1786,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),some divide and conquer algorithms are\nquicksort\nbucket sort\nradix sort\nbinary search\n,2.5
-1787,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort and quick sort is the the example of divide and conquer.,2.5
-1788,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),binary search\nmerge sort,2.5
-1789,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"divide - merge sort ,",2.5
-1790,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge Sort, \nQuick Sort,\nBinary Search",2.5
-1791,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort,binary search",2.5
-1792,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),some examples of divide and conquer algorithms are:\nMerge Sort\nQuick Sort\nBinary Search,2.5
-1793,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"binary search , merge sort, quick sort",2.5
-1794,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),quick sort and merge sort,2.5
-1795,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),binary search\nmerge sort,2.5
-1796,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort\nQuick Sort\n,2.5
-1797,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort , quick sort are some examples of divide and conquer algorithms.",2.5
-1798,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, quick sort, Closest pair of points.",2.0
-1799,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, quick sort and binary search",2.0
-1800,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort\nQuick Sort,2.0
-1801,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort , binary search ",2.0
-1802,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, insertion sort",2.0
-1803,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort , quick sort, closest pair problem,   are the examples of divide and conquer algorithm",2.5
-1804,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, quick sort are some examples of divide and conquer.\nFinding closest pairs in a plane is a problem in which we can apply the divide and conquer approach.",2.5
-1805,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),sorting techniques like quick sort  and binary sort can also be an eg,2.5
-1806,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer are:\n1- Binary Search\n2- Merge sort\n3- Quicksort,2.5
-1807,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Quick sort , merge sort",2.5
-1808,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Solving the n queen problem, merge-sort",2.0
-1809,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),used in MERGE SORT here the array is divided into two halves and smaller of the two values \ngets matched at the end.\nused in BINARY SEARCH here we first sort the array and find the mid element and place the right element in its \nright position and reducing our complexity which reduces to O(LOG N)\n,1.0
-1810,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),One good Example of divide and conquer is binary Search in which we divide our search space accordingly and doing operation on that required search space.\nOther examples are:\n->Quick Sort\n->Ternary Search (somewhat extension of binary search,1.0
-1811,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\nquick sort,2.0
-1812,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples of divide and conquer algorithm are binary search , dynamic programming. It is used when same subproblems are not evaluated many times.",2.0
-1813,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\nquick sort\nradix sort,1.0
-1814,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Binary Search\nQuicksort\nMerge Sort\n,2.0
-1815,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Quick Sort\nMerge Sort - The Array is subdivided to smaller arrays and after reaching the unit array they are combined.\nBubble Sort- An array element constantly being compared with previous array element then swapping takes place.\n,1.0
-1816,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer are Merge sort and quick sort,1.0
-1817,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, N-queen, rat in a maze",1.0
-1818,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, binary search",2.0
-1819,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge Sort, Quick Sort, Radix Sort,",1.0
-1820,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\nbinary search,1.0
-1821,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Divide and Conquer utilizes solving of a problem by breaking down the data set into smaller subsets and then performing the desired operations on it. And then subsequently, perform it again and again while bringing it all together to achieve the final solution.\nFollowing are some examples\n1) Quick Sort\n2) Merge Sort",2.0
-1822,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points), whose complexity is better than the brute force approach.,1.0
-1823,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1. Merge sort\n2. Binary search \n3. Quick sort,2.0
-1824,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),divide and conquer is used in binary seach for eg. to search a page in dictionary\nalso in merge sort eg. to sort an array of elements.,2.0
-1825,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort , quick sort, Closest Pair of Points\nStrassen’s Algorithm of matrix multiplication",2.0
-1826,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"There are various approaches to develop an algorithm such as divide and conquer ,backtracking .\nThe algorithms which is developed using the divide & conquer techniques involve three steps: Divide the original problem into various subproblems. Solve every subproblem individually, recursively. Combine the solution of the subproblems into a solution of the whole original problem.\nThe algorithm which is developed using backtracking involves moving to the answer at each step and note the other available paths for that step ,if it reaches the output then its good otherwise it retracts its steps and try other available paths to reach the output.",1.0
-1827,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, Binary Search",1.0
-1828,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),selection sort\ninsertion sort \nquick sort\nmerge sort,2.0
-1829,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Divide and conquer includes dividing the array in parts and then apply algorithms. Examples are - Merge Sort & Quick Sort,1.0
-1830,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),quick sort and merge sort are examples of divide and conquer.,1.0
-1831,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points), Binary Search\nMerge Sort\nQuick Sort,1.0
-1832,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort\nQuick Sort\n,1.0
-1833,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"binary search, merge sort, quick sort",2.0
-1834,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),,
-1835,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Binary search, Merge sort",1.0
-1836,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"binary search, interpolation search finding square root or cube root of a number and many more.",1.0
-1837,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"sorting of array using quicksort, stock buy and sell  to attain max profit .",1.0
-1838,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),The examples of divide and conquer algorithms :-\n1) Quick Sort\n2) Merge Sort\n3) Binary Search\n4) Interpolation Search,1.0
-1839,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Algorithms are developed by first-\nUnderstanding the problem, Finding the solutions from pre-existing algorithms, If not available then finding the new algorithm, design the optimal algorithm",1.0
-1840,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),in divide and conquer algorithm merge sort algorithm and quick sort algorithm comes .,2.0
-1841,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),,
-1842,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort,  closest pair point.",1.0
-1843,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),those algorithm that can be perform by dividing a problem into small parts and then solved separately those problems comes under divide and conquer algorithm  ,1.0
-1844,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge Sort uses divide and conquer strategy, as well as Binary search in we keep dividing or search space into 2.",1.0
-1845,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),The examples are :\nmerge sort \nquick sort,2.0
-1846,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer technique is that\na}It is Used in binary search of an element\nwhich has many advantages in real life such as counting the occurence of a number ,2.0
-1847,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"some examples are mergesort , quicksort etc.",1.0
-1848,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"binary search, quick sort, merge sort, strassen multiplication are some examples of divide and conquer algorithms",1.0
-1849,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"binary search ,median search ,different sorting algorithm like quick sort, merge sort",2.0
-1850,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\n,1.0
-1851,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples of divide and conquer :-\n1. merge sort \n2. quick sort ,1.0
-1852,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer algorithms are binary search, merge sort, etc. These problems are the ones where we solve a smaller part of the problem first and then club all those results together to get the solution to the bigger and the main problem.",2.0
-1853,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),binary search,1.0
-1854,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Simply,  when we want to shorten our problem we use divide and conquer like when we calculate the time complexity of a recursive recurrence relation we use divide and conquer , in binary search ,in merge sort , etc.",1.0
-1855,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1.binary Search\n2.quick sort\n3.merge sort,2.0
-1856,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),,
-1857,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, quick sort, binary search, ",1.0
-1858,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples of divide and conquer algorithms are binary search , sorting techniques like merge sort, quick sort, etc. ",2.0
-1859,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples are: Binary search, Merge sort, etc.",2.0
-1860,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort, quick sort are some of the examples of divide and conquer algorithms.",2.0
-1861,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\nquick sort\nbinary search,2.0
-1862,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Binary Search, Merge sort , Quick sort are some examples of divide and conquer algorithms.",2.0
-1863,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),binary search\nquick sort\nmerge sort,2.0
-1864,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some basic examples of divide and conquer are : Merge Sort ,  Finding the shortest path between two points etc",2.0
-1865,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"binary search, merge sort,etc.",2.0
-1866,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer algorithms are as follows: \nQuick Sort. Merge Sort, Greedy Algorithms including Knapsack, Travelling Salesman Problem, ",2.0
-1867,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1. Merge Sort\n2. Traveling Salesman\n3. Coinage\n4. 0/1 Knapsack,2.0
-1868,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Divide and conquer examples-\nMerge Sort\nQuick Sort\nGreedy technique\n,1.0
-1869,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1. in railways to sort   which train will come on which platform and at what station \n2. To find information about particular in seconds while a person will take days to find in a company,1.0
-1870,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer algorithms are merge sort, quick sort, binary search. ",1.0
-1871,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort\nQuick Sort\nBinary Search,1.0
-1872,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort and binary search uses divide and conquer algorithms.,1.0
-1873,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"There are some sorting techniques such as merge sort, quick sort and some searching techniques such as binary search.",2.0
-1874,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort\nQuick Sort,2.0
-1875,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples of divide and conquer are:\nmerge sort\nclosest pair of points.\nsprinklar problem.,2.0
-1876,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort , helix sort , selection sort ",2.0
-1877,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of divide and conquer algorithms are:\n,1.0
-1878,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),In divide and conquer algorithms we use to divide the things into smaller ones and then conquer for example:-  ,1.0
-1879,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),binary search \nquick sort\nmerge sort\n,1.0
-1880,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Binary Search can be the example of divide and conquer algorithms.,1.0
-1881,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Examples of divide and conquer algorithm are:\nmerge sort, quick sort, binary search, etc.\n",1.0
-1882,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples of divide and conquer algorithms are merge sort and quick sort,1.0
-1883,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, quick sort, insertion sort.",1.0
-1884,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, quick sort, binary search",1.0
-1885,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer algorithms are BINARY SEARCH , MERGE SORT AND QUICK SORT",1.0
-1886,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort and quick sort is the one of the most suitable example of divide and conquer algorithm.,1.0
-1887,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),some examples of divide and conquer stratergy is :\n1. merge sort\n2. quick sort\n3. insertion sort,1.0
-1888,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1. Binary search\n2. Ternary search\n3. Merge sort\n4. Interpolation search\n,1.0
-1889,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1). Merge Sort\n2).Quick Sort\n3).Binary Search\n4).Linear Search,1.0
-1890,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"closest pair of points, merge sort and quicksort.",1.0
-1891,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"1. Quicksort is a sorting algorithm. The algorithm picks a pivot element and rearranges the array elements so that all elements smaller than the picked pivot element move to the left side of the pivot, and all greater elements move to the right side. Finally, the algorithm recursively sorts the subarrays on the left and right of the pivot element.\n2. Merge Sort is also a sorting algorithm. The algorithm divides the array into two halves, recursively sorts them, and finally merges the two sorted halves.\n3. Closest Pair of Points: The problem is to find the closest pair of points in a set of points in the x-y plane. The problem can be solved in O(n^2) time by calculating the distances of every pair of points and comparing the distances to find the minimum. The Divide and Conquer algorithm solves the problem in O(N log N) time.\n4. Strassen’s Algorithm is an efficient algorithm to multiply two matrices. A simple method to multiply two matrices needs 3 nested loops and is O(n^3). Strassen’s algorithm multiplies two matrices in O(n^2.8) time.\n\n",1.0
-1892,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort,1.0
-1893,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge sort \nQuick sort\nare the some examples of  divide and conquer algorithms,1.0
-1894,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"examples of divide and conquer algorithms are manly search and sorting algorithms \nbinary search, linear search , merge sort, quick sort",1.0
-1895,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Common operations that can be performed on a data structure are input of data, and manipulation of data. Manipulation of data takes place via different methods depending on our algorithm and what we use as such. Ex- sorting a data, finding the element with the max or min value in the data structure,  finding common elements in the data structure etc.",1.0
-1896,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge Sort,Quick Sort,",1.0
-1897,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Binary search\nmerge sort\ninversion count\nquick sort,1.0
-1898,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1.) The one of the most known sorting technique MERGE SORT .\n2.) Binary Search \n3) Quick Sort,1.0
-1899,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Few examples of D& C are;\n1.Merge sort;\n2.Binary Search;\n3.Stock and sell problem;\n4.Quick sort\netc.,1.0
-1900,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples are:\nRecursion\nQuick sort\nBinary search,1.0
-1901,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Quick sort and merge sort.,1.0
-1902,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge Sort \nQuicksort \nStrassen's Matrix Multiplication,1.0
-1903,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),some examples of divide and conquer algorithm are-;\n1-quick sort\n2-merge sort\n,1.0
-1904,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),examples:-\nmerge sort\nbinary search,1.0
-1905,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort and binary search are examples of divide and conquer algorithms.,1.0
-1906,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\nquick sort \nbinary search,1.0
-1907,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer algorithms are quick sort, merge sort ,binary search.",1.0
-1908,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort ,quick sort",2.5
-1909,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"network flow algorithm, graphs etc",0.0
-1910,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some example of divide and conquer algorithms are-\nmerge sort, binary search.",2.5
-1911,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"examples for divide and conquer algorithm is merge sort ,quick sort,",2.5
-1912,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"binary search, merge sort, quick sort",2.5
-1913,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),In divide and conquer algorithm we first divide the problem in many sub-problems and then try to solve the sub-problems and in the end we try to merge everything together to finally solve the bigger problem.,1.0
-1914,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\n,2.0
-1915,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Binary Search, Merge Sort ",2.5
-1916,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge sort And Quick sort ,2.5
-1917,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),in railway network \nin stock exchange problem,0.0
-1918,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples include: Merge sort and quick sort algorithm,2.5
-1919,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer algorithms are binary search, merge sort etc.",2.5
-1920,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),quick sort algorithm\nmerge sort algorithm,2.5
-1921,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1. binary search\n2. quick sort\n3. merge sort,2.5
-1922,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),,0.0
-1923,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1)Merge Sort\n2)Quick Sort\n,2.5
-1924,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),quick sort and merge sort ,2.5
-1925,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1. Merge sort\n2. Quick sort.\n3. Binary search.,2.5
-1926,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort\nquick sort\nare the examples of d and c algo.,2.5
-1927,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),,0.0
-1928,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),divide and conquer approach is to break the problem statement into parts and solve them separately \nfor eg:- addition and multiplication of 2 linked lists,1.5
-1929,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1:- Merge sort \n2:- Quick sort\n3:- N-queen ,2.0
-1930,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some of the examples of divide and conquer algorithms are :- \n1.Merge Sort\n2.heapify,2.0
-1931,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),sorting and searching,2.5
-1932,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"merge sort ,quick sort ,binary search ",2.5
-1933,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples are:-\n1. Merge sort\n2. Quick sort,2.5
-1934,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort \nbinary search \n,2.5
-1935,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Google , Amazon , etc are some example of divide and conquer algorithm.\nmerge sort algorithm is used in divide and conquer.\n",1.5
-1936,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),quick sort\nmerge sort,2.5
-1937,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),binary search\nmerge sort\nsubarray sum problem\nquick sort,2.5
-1938,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Binary Search \nMerge Sort\n,2.5
-1939,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Merge sort, binary search, quick sort.",2.5
-1940,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),example of divide and conquer algorithm merge sort and quick sort,2.5
-1941,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Example of Divide and Conquer algorithms are :-\n1) Quick sort\n2) Merge Sort,2.5
-1942,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Some examples of Divide and Conquer Algorithms are Binary Search and Merge Sort. ,2.5
-1943,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"Some examples of divide and conquer algorithms are Dijkstra algorithm, ford Fulkerson algorithms,",0.0
-1944,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),merge sort \nquick sort,2.5
-1945,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"in divide and conquer algorithm , we first divide the elements then take it one by one step and then write in right manner. ",1.5
-1946,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),Merge sort\n,2.5
-1947,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),when we are doing aeration in which large database is concern and we have to find out the certain function from that database we use divide and conquered \nlike during searching someone profile on social media we can use divide and conquer it had database which divide a system into a serial potion and sech on that bases ,2.0
-1948,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),some of the applications of divide and conquer algorithms are used in:\n1) Merge sort\n2) Quick sort,2.5
-1949,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),divide and conquer algorithms we solve big problem it sub parts example matrix multipi,2.5
-1950,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),master theorem.,0.0
-1951,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),1)merge sort\n2)quick sort,2.5
-1952,What are some examples of divide and conquer algorithms?,The below given problems find their solution using divide and conquer algorithm approach – (1) Merge Sort (2) Quick Sort (3) Binary Search (4) Strassen's Matrix Multiplication (5) Closest pair (points),"examples of divide and conquer\nrat in a  maze,\ntravelers theorem\nHamiltonian  graph\n",0.0
-1953,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack uses the LIFO (last in first out) principle that is elements that are added last are deleted first. There is only one same path of adding and deleting elements, elements are stacked on top one over another and the element at the top of the stack is deleted first. ",2.5
-1954,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks uses LIFO principle i.e. last in first out we use stacks to store the recent data in order to access it as fast as we can. it stores the values at the compile time,2.5
-1955,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is a data structure in which first element which is inserted in a stack comes out last it follows  LIFP appoch \n,2.0
-1956,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks because:\n1) They do not require limits like arrays\n2) They provide LIFO process (Last In First Out) that reduces time complexities of many algorithm and operations,2.5
-1957,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack offers a methodology called LIFO which stands for Last In First Out. For ex, website navigator. Last website visited is brought first when we click back. Undo also does the same.",2.5
-1958,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack are LIFO data structure used to store data in last in and first out form. Stack are used in place of array to reduce space complexity and operation like push and pop are easy to implement on stack.,2.5
-1959,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",a stack is used in evaluating expressions consisting of operands and operators. ,2.5
-1960,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack gives  last in first out operation. Stack helps in finding the parenthesis of the expression,2.0
-1961,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is a linear data structure which follows last in first out method and is used for many purposes like parenthesis and many more,2.0
-1962,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-1963,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are the linear data structure that are used to store the values of a particular data ,in LIFO format i.e.  Last In First OUT ",2.5
-1964,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are used , when we need to perform the operation on every part of string or any number and make better use of it . Stacks basically have insertion and deletion both from the top , it follows FIFO approach.",1.0
-1965,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack for storing information in FIFO form(first input first output).,1.0
-1966,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a data structure that is ideally used to store data in a linear format ,1.5
-1967,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks to implement the LIFO ( last in first out ) technique.,2.5
-1968,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack Data structure follows LIFO (Last In First Out) Rule. We use stack in :- \n- Depth First Search\n- Inorder Traversal of binary tree.  \n- Finding postfix expression,2.5
-1969,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are based on the principle of LIFO i.e last in first out\n\nthe data that was entered in the last will be retrieved first,2.5
-1970,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are the data structure which works on the principle of \,1.0
-1971,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used to compute to prestore some values and then compute the function given.it helps to give element from top and end  in much less complexity.,1.0
-1972,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to easily push the element in the list.,1.0
-1973,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to temporarily store elements in an order which we can use later on. In other words it is a temporary holder of elements and \nmany other operations can be conducted with the help of stacks.,1.0
-1974,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to store data in an order which we can utilise later. The various operations that can be perfor,1.0
-1975,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are the type of data structures which uses the mechanism of Last In First Out.They are helpful in storing large amount of data and is used in the algorithms which takes high space complexities.,2.5
-1976,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a data structure which follows LIFO( last in first out ). We use stacks when we have to extract something in reverse order of its insertion. ,2.5
-1977,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","as stack is LIFO ,  we need a stack to get some of our operations in constant time . like insertion , deletion ",2.5
-1978,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks use LIFO (Last In First Out) approach which means the last element to be inserted gets deleted first. Stacks are used in various algorithms when we want to know the last element that was inserted or want the delete the latest element inserted. Stacks are used in recursion as the function waits for the answer from the latest recursive function callled.,2.5
-1979,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks use the LIFO (last in first out) approach which helps when our requirement of the problem requires us to have to delete the element inserted most recently.,2.5
-1980,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to store the element/ data on LIFO basis. ( When the most recent information is required in the solution ). We use stacks in DFS where we are traversing the data using stack data structure.,2.5
-1981,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack are the data structure used to store data inside it. Stack are used when we want to pop the that data first which is pushed at last. It follows first in last out approach. ,2.5
-1982,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","We use stacks as per our requirement in a problem, when we want to address the last inserted value first (LIFO). Eg: Depth First Search",2.5
-1983,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are the data structures which are used to store the data in it and is used to delete some data or insert some data.,2.5
-1984,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks is also known as last in first out(LIFO). ,2.5
-1985,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-1986,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks is a special data structure with some functions similar to array. stacks help to store data from top and result out the data in different order is called stacks convert the arrangement of values from ascending to decreasing form.```,2.0
-1987,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-1988,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are used to add/remove elements in a particular order. Stacks can also be used for backtracking algorithms.,2.0
-1989,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks to add or remove elements in particular order.,2.5
-1990,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a kind of Data Structure which is used for storing the data . Stack is very efficient to store the data if we want to store the data in order . Stacks are a better option to use over linked list because these can also be used in STL and we can easily perform many operations (like insertion and deletion ) in stack which is difficult to use in linked list .\nStacks use LIFO(Last In First Out) method .,2.5
-1991,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks when we want to represent data in the vertical form nd use functions which follows the principle of \,2.5
-1992,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks when we have to store the data in a vertical format or when we havew to access it on the LIFO principle (last in first out). We can use several built in functions to deal with the data stored in stacks for accessing the top most element or delete it.,2.5
-1993,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack works on the principle of Last In First Out (LIFO). IT is used to store object on which we want to perform operations later on and operations on those objects first which entered last.,2.5
-1994,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks can be used in conditions where we need to use the concept of first in first out.(FIFO) Examples of places where stacks can be used are undo/redo operations etc.,1.5
-1995,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stacks for multiple use cases, for sorting, for prefix and post fix notation",2.5
-1996,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","We use stacks when we have to use the First In First Out (FIFO) approach. In stacks the element gets added to the top of the stack and the last element is removed when we want to delete (pop). Stacks are also used to solve the infix, prefix, postfix expressions.",1.5
-1997,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","A stack work for the retrieval of last element in the first order....It works on the basis of first come , last serve.",2.0
-1998,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to store data in form of chronological order so as to retrieve the data accordingly.  Stack allows us to use the memory efficiently by maintaining the top variable and keeping the count of data.\n ,2.5
-1999,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks when we want to work on the that last inputted value at the begining of our program as it used the property of LIFO (Last in First Out),2.5
-2000,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack whenever we require FIFO(First In First Out) property in our solution.\n,2.0
-2001,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack whenever we need FIFO(First In First Out) property in our solution.,2.0
-2002,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are used to fulfil the need of FIFO(First in First out) in the solution.,2.0
-2003,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are the data structures that are used to store large number of data.,1.5
-2004,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack are the type of data structures which are used to store large data.,2.5
-2005,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","it is a linear data structure in which elements are accessed, inserted and deleted from one end called the top of the stack. Stack follows the LIFO  approach. Two basic operations on a stack are push and pop.",2.5
-2006,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",One good use of stack is to remove the implementation of recursion as it can recursively provide data instead of having to call a function again and again.,2.5
-2007,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are a linear data structure that works in Last In First Out method .They are used to store data and solve problems that require reversing and sometimes in trees.,2.5
-2008,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are useful data structures which follow LIFO (last in first out) . This can be be helpful in algorithms like depth first search in binary trees. ,2.5
-2009,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is a form of data structure which is like putting plates over another place it completely overlaps and whenever required we use the topmost plate.... similarly to store data in such a form where after storing we get obliged by last go first out format we use stack,2.5
-2010,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are used for LIFO purposes. LIFO stands for Last In First Out.\nIt is used to explore deep branches of trees and graphs. For example - Used in depth-first searching,1.0
-2011,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack follows the last in first out approach. We use stacks to keep track of the order of operations\nfor eg. in a compiler the functions that are called last would finish first\neg2. to check parathesis, stacks are used\nDepth first search is used using stacks",2.5
-2012,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",1)for LIFO-last in first out\n2)problems are solved in order\n3)no direct jump to another function before completing one,2.5
-2013,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","to add or remove an element , it is FIFO",1.0
-2014,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Last in first out\nwe can use the data structure where we need to enter in last and take it out first,2.5
-2015,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",when we have use the functionality of first in last out then we use the stacks ,2.5
-2016,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stacks to store data, which can be used later on. It uses the technique of last come first serve ( LIFO).\nexample: function calling uses stack implementation",2.5
-2017,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","We use stacks to insert data in a LAST IN FIRST OUT manner, it enables us to extract the piece of data that was inserted at the last . We can use top for getting the information regarding the data present on the top of stack and push , pop could be used respectively for insertion and deletion.",2.5
-2018,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","stacks is a data structure used to store in the values, it used the concept of LIFO (last in first out) and ejects values accordingly. It is used in searching algorithm and also in tries.",2.5
-2019,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks for storing the data linearly in such a way that we can use the oldest  information first for our purposes as it is a LIFO(Last in first out)Process.,2.5
-2020,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc."," A stack is a linear data structure in which elements are accessed, inserted and deleted from one end called the top of the stack. Stack follows the LIFO ( Last In First Out) approach. Two basic operations on a stack are push and pop.",2.5
-2021,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are useful if we need to add or remove something in an array in an particular manner ,1.0
-2022,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",It is an easy way to store and access elements that only need to be used once.,1.0
-2023,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We can use stacks to make Last In First Out(LIFO) programs. ,2.5
-2024,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stack to store memory temporarily, it uses LIFO which is helpful to recover data if lost.",2.5
-2025,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Application of stack is : In depth first search\nRecursive functions also use stack.,2.5
-2026,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","A Stack can be used for evaluating expressions consisting of operands and operators. Stacks can be used for Backtracking, i.e., to check parenthesis matching in an expression. It can also be used to convert one form of expression to another form. It can be used for systematic Memory Management, and even piling up of data.",2.0
-2027,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is used when we want to use the last element first as stack works on the principle of last in first out.,2.5
-2028,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Whenever we are in a need to store data linearly and have a tendency to take out the last inserted there we prefer to use stacks .,2.5
-2029,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a last in first out data structure which is commonly used for storage of information and in some algorithms as well.,2.5
-2030,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks when we have to store the data in lifo fashion(last in first out)\nstacks are the data structure used in the background for backtracking and other operations,2.5
-2031,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack or queue instead of arrays when we want the elements in a specific order. The order can be LIFO or FIFO anything.,2.5
-2032,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","stack is a data structure that follows LIFO. thus, the data on top will be out first, kind of like a bucket.\nstacks can be used to store temporary data files",2.5
-2033,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack to calculate the operations like DFS we use stack so that first er focus on the deepth stack is first the last in first out is used in stack in which we traversal on each element ,2.5
-2034,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks is a data structure following the rule LIFO\nThe element entered in the last will the first one to come out\nStacks can also be used to make better algorithms for different data structures,2.5
-2035,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are last in first out based .\nthey can be understood as a pile of books \nthey are real life possible thing.\nwhen constrains of lifo are applied in a 1-D array it becomes a stack we use stack for organsing are data as examle:the recent incident is ought to be searched more in google this way we can store the data in stack and the recent input will be the answer reducing the time complexity,2.5
-2036,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",A Stack can be used for evaluating expressions consisting of operands and operators. Stacks can also be used for Backtracking.,2.5
-2037,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","stacks works on last in last out which can be used graph traversal , prefix suffixes etc.",1.5
-2038,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a LIFO data structure that is last in first out.We use stack to keep track of memory and space used in a program so that  when a stack overflows is gives us an error.,2.5
-2039,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack uses last in first out technique so we use it when we need to access most recently entered data first,1.0
-2040,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks is a LIFO (last in first out) data structure in which it returns the last value stored in the stack when we pop out the top value and returns -1 if the stack is full.,2.5
-2041,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is used for backtracking. ,1.0
-2042,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack can be used for backtracking.,1.0
-2043,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks when Last In First Out method is used. ,2.5
-2044,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack implements Last In First Out(LIFO). Whenever we need this situation we use stacks.,2.5
-2045,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are the data structure that performs operation in FILO, we majorly use stacks to use this operation. ",1.0
-2046,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack for the use of first come last out approach. ,1.0
-2047,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",it is a efficient data structure that has a property of last in first out (LIFO) this property helps to perform certain action easily that why we use stacks,2.5
-2048,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a data structure that follows the LIFO (Last In First Out Rule). A stack can be implemented using an array or a linked list. Stacks have different applications. \n1. It is used in Depth First Search (DFS) traversal of a tree.\n2. Recursive functions can be used to convert into iterative using stacks.,2.5
-2049,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used to carry out and evaluating the operations and operands,1.0
-2050,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.", Stack is a data structure that stores the data according to FIFO (First In First Out) principle i.e. the data can be accessed through one way only. The data item that was stored at last will be accessed first. It is very useful while performing various searches.,2.0
-2051,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack so that we get the information about memory or space like when we get stack overflow,2.5
-2052,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks because it is a linear data structure and is of the type last in last out. If we want to quickly store a value which needs to be accessed immediately afterwards (and discarded) we can use it. It is especially useful in divide and conquer strategy of algorithm solving. ,2.5
-2053,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","stacks works on LAST IN FIRST OUT, the element that has put in the last we have to access it first\nlike in case of DFS traversal of graphs.",2.0
-2054,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack is a data Structure in which we can say our data is stored in stack ,(pilled up)\nIt is LIFO type Data Structure(Last in First Out)\n|      6   |\n|     5    |   --> illustration of Stack\n|___4_ _ |\nit is used when we need to excess data which we entered latest.",1.0
-2055,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",when we are told to solve a question which demands first in first out approach or required to pop or put data in top to down manner then we use stacks.,2.0
-2056,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.", A stack can be used for evaluating expressions consisting operands and operators. It can be used for backtracking. It can be used to convert one form to other form.,2.0
-2057,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",so that we can remove first inserted element at last. as it works on last in first out  principal last inserted element will be removed at first.,2.0
-2058,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","A Stack can be used for evaluating expressions consisting of operands and operators. Stacks can be used for Backtracking, i.e., to check parenthesis matching in an expression",1.0
-2059,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks is based upon first in first out principle or FIFO. The data that you are inserting in stack would be pushed out first after that the second, the third  and so on.",1.0
-2060,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a type of data structure to store the data. It works on the principle of first in first out. So if we need to store data and then retrieve it in opposite order of the values places then we should use stack data structure.,1.0
-2061,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used when we want to use the last in first out principle as stacks works on LIFO.,1.0
-2062,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack uses LIFO approach (Last in first out) that means we can stack our data and the recent data is called first. Stack has some major applications in the understanding of recursion.,1.0
-2063,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks works on the principle of LIFO: Last in First out. This approach of retrieving the data and using it for it other purpose is useful in many problems and helps to reduce the steps of our solution. The stack module of STL library has predefined functions which reduce the effort of writing the most used pieces of code.,1.0
-2064,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",it is data structure works on last in first out basis.\nwe use  it for\n1 expression evaluation-for evaluating arithmetic operations\n2undo/redo functions\n3.for backtracking and recursion,2.0
-2065,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks follow the Last in, First out procedure. So they are basically used to maintain the record whenever we need to fill a dataset and use these properties. ",1.0
-2066,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Merge sort, N queens problems, Closest pair problem",1.0
-2067,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","We use stacks wherever Last in First out property is required.\nFor example in parenethesis matching problem, stacks are used to store opening parenthesis until closing arrives.",2.0
-2068,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used to arrange data in first in last out fashion.,1.0
-2069,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Whenever we need to access the data entered at last in a data structure first of all,1.0
-2070,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Merge sort ,1.0
-2071,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are data structures that are based on the principle of Last In First Out (LIFO). We use stack to implement techniques such as recursion, some other examples are : Tower of Hanoi , Reversing Linked Lists etc.",1.0
-2072,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks rather than array to use elements in a specific order,2.0
-2073,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are like a block in which we can push our elements. Then, we can find the top most element, pop out top element etc. It's time complexity depends on the operation but space complexity is O(n) as we are taking memory. Stacks can be helpful in questions like we have to find a count of a number, to check duplicate numbers. In such questions, we can pass each and every element of array in a stack. Then start popping out top element with necessary conditions.",1.0
-2074,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks work on the principle of LIFO (last in first out). Stacks can be used to  record the track of elements stored.,1.0
-2075,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use  stacks to do the questions or algorithms in which we have to push the first element as pop it at last. as stack is filled bottom to top .\nLast in First out property,1.0
-2076,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks when we need to perform the LIFO (Last In First Out) operation in a particular problem.,1.0
-2077,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",it used in the cases for which we want the operation \,1.0
-2078,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a linear data structure that follows LIFO method to perform all the operations.,1.0
-2079,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are used to store data that are in order. They work on the principle of LIFO ( Last in First Out ). So , elements are stored when an array or any data structure is traversed and the last element traversed is stored on the top and can be retrieved first. This helps in accessing the recent or last data being stored and can be operated upon.",1.0
-2080,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is a data structure which the principle of LIFO (Last in First out). which can be seen in many places in daily lives like stack of a plate. sometimes this technique is used to solve various problem like like problem in which 3 rods are given and we have to move the discs from one rod to another and arrange them in a particular manner.,1.0
-2081,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used to keep the record of the element .It follow the LIFO approach last come first out which mean the last element which inserted in stacks will come out first.,1.0
-2082,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are used for their FILO (First In Last Out) or LIFO (Last In First Out) properties.,2.0
-2083,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Merge Sort and problems such as shortest distance on 2D graph are the examples of divide and conquer algorithms.\n,2.0
-2084,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","stacks used LIFO (last in first out ) approach , we use stacks for comparison of words, string , for example we use stack in string matching algorithm . ",2.0
-2085,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",they are data structures used for storing data mostly in the form of first in last out order. \nyou can push and pop data nodes according to youyr requirements.\n,2.0
-2086,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","it is the faster,   it is the use very  easy",2.0
-2087,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack for easy pushing and popping of an element the working of an stack is like a element is pushed at the end of the stack and one element popped from the top that we need ,2.0
-2088,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack to solve problems where the data moves in a firs-in-first-out manner i.e. the data we push or insert in the stack last should pop or get removed first.,1.0
-2089,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are the data structure in which uses the principle of last-in-first out (LIFO) .it uses the push function (to insert the element) and pop function.,1.0
-2090,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks as it uses to implement  LIFO technique last in first out technique and it can be very useful to solve mathmatical equations ina very efficient ways,2.0
-2091,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are used to solve problems like undo text, ",1.0
-2092,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",depth first search uses stack data structure in its execution,1.0
-2093,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","stacks work on the principle of last in first out(LIFO),thus are useful when we want to remove elements in a this manner.",1.0
-2094,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",for last in first out kind of operations,1.0
-2095,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is used to implement last in first out ( LIFO) . the element we enter ,1.0
-2096,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks in the cases where we need to get the data from only one direction. It works on the principle of LIFO so in any case where we want to store the data in a linear data structure and we only need to get or access the element that was last inserted in in we use Stacks. It is very commonly used in search algorithms for Graphs. ,2.0
-2097,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are LIFO (Last In First Out) operators. Stack are used in balanced parenthesis problems where we have to check if the opening parenthesis are balanced by the number of closing parenthesis.,2.0
-2098,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a linear data structure which follows the LIFO principle which says that last in first out and in many problems we need LIFO principle and we easily solve those problems by using stack.,2.0
-2099,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",when we have problem where we have to implement last in first out,2.0
-2100,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are used in order to store data such that only 1 field is required at that time. As the name suggests, its just like stack of blocks we can say storing the data.\nTo take an example in order to make it more clear we use stacks in undo and redo operations.  like in undo it gives what was the last step done.  ",2.0
-2101,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","stacks are used to arrange the function calls in any problem, where we use the concept of last in first out, that is it for example there is a function which is has a function call of a print function within so it stops the execution right away and puts the print function on top now and solves it first and then goes back to the original problem.",1.0
-2102,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks as it eases the process of algorithm as the elements get arranged like stacks.,1.0
-2103,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks basically follows the principle of LIFO that is last in first out. And at many places we want this principle and hence we use stack there. Example where we use stack is  depth first search.,1.0
-2104,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks work on the principle of LIFO that is Last In First Out. So whenever we deal with a problem where we need to deal with the current element first we would use stack.,1.0
-2105,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks follows first in last out property. so where we need to perform an operation where this type of operation is needed we use a stack.\n,1.0
-2106,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack are the data structure use to store the element in such a way that the element which will come in Last, will get out firstly.\nIt obeys LIFO property and hence whenever we need it we use it.\nSome examples of use of stack are Depth First Search, For doing preorder or inorder traversal iteratively without using internal stack space, For solving any recursive problem, the system internally uses the stack.\n",1.0
-2107,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",in stacks as we use LIFO last in first out therefore it helps in problems where we need the data we entered last before others.,1.0
-2108,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks in cases when we need to insert the data from front and also take it(pop) from front. For instance in a library book management system we can use stacks for storing the data regarding the books. ,1.0
-2109,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stacks to store the elements , then we can pop ,push the element , find the topmost element, etc.",1.0
-2110,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks uses the LIFO technique to form a linear data structure. ,1.0
-2111,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to store data in the \,1.0
-2112,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is used when we require a linear data structure which follows LIFO technique. it is very useful when we need to apply recursion in program.,1.0
-2113,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used for finding the particular order in whcih actions are perfomed.,1.0
-2114,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack is based in Last In First Out property. These are at the basic level arrays only and can be implemented from the same. These are useful for performing quick operations like push, pop and where we need to remember the last element at which we were as it makes accessing the last element easier than in an array. ",1.0
-2115,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack follows FILO - First In Last Out rule.,1.0
-2116,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used first in first out problems. the data enter first is pop out first,1.0
-2117,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",because it follows LIFO rule.,1.0
-2118,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are used to solve the problem where the data moves in FirstIN LastOUT manner,1.0
-2119,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to store the data with last in first out system.it is used as a container to store the data items.,1.0
-2120,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-2121,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack because:\na)to organize the data (large data)\nb)to provide the best time complexity\nc)to make our question easier,1.0
-2122,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to store the data in form of LIFO i.e Last in first out,1.0
-2123,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks because of the feasibility of use and we also use it for back tracking.\nit also helps in converting one form of equation to another from.\n\n,1.0
-2124,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks can be used mainly for backtracking. It can also be used for convert one expressions to another expressions.,1.0
-2125,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is basically used for last in first out scenarios. It can be used in backtracking for eg. to check parenthesis matching in an expression,1.0
-2126,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack to solve the algorithmic problems that require LIFO (Last in first out) data structures,1.0
-2127,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks use LIFO operation which means Last data in will be displayed first. It can be used in DFS tree traversal. ,1.0
-2128,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack for applications that need first in last out type applications. It is a data structure that stores data in such a way that data entered last leaves first. It is also used to remove recursion.\n,1.0
-2129,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack data structure follows the principle where the first input is will be output last . It is used where we need to store the previous information but the operation we are asked to perform on the last entered input . LIKE STACK DATA STRUCUTURE IS USED IN DFS TRAVERSAL OF GRAPH .,1.0
-2130,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is used to store data in first in last out manner.,1.0
-2131,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack are lifo operation which means the data we entered at last will come out at first.,1.0
-2132,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack is a data structure which has property of last in first out (LIFO), so we can access only top element. It is widely used in recursive approaches such as devide and conquer algos as it stores all the recursive calls made. It is also used in evaluating prefix and postfix notations.",1.0
-2133,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are needed to insert and pop the data which it does in constant time complexity. It follows the LIFO(last in first out) fashion.\nInsertion and deletion of the data is very simple in stacks. There are various other operations that a user can perform by using stacks.,2.0
-2134,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack used when we want to follow the order of LIFO. last in first out element arrive in last will remove at first.,2.0
-2135,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks work on last in first out basis. We use stack or queue instead of arrays/lists when we want the elements in a specific order i.e. in the order we put them (queue) or in the reverse order (stack). \nstacks are dynamic while arrays are static. So when we require dynamic memory we use queue or stack over arrays,2.0
-2136,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack when we have to process data in linear manner,2.0
-2137,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack Data structure is of LiFo type data structure i.e. \nLast in first out type it is used in many algorithms but the main use of stack is while doing problems \nthat use recursion as the last call of any recursive function will be executed first,2.0
-2138,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are used on the concepts of last in first out which includes a data structure in which element which is added first come out last and the element which is inserted at lsst is first out,2.0
-2139,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","The approach to develop algorithms depends on the problem we are solving that is given to us. An appropriate data structure is chosen, multiple of them can be used as such. And then we try to reach the end goal of the solution using the least amount of traversal or time complexity and manipulate our data accordingly.   ",2.0
-2140,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","A Stack can be used for evaluating expressions consisting of operands and operators. Stacks can be used for Backtracking, i.e., to check parenthesis matching in an expression. It can also be used to convert one form of expression to another form. It can be used for systematic Memory Management.\n",1.0
-2141,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",LIFO data structures. Last in First Out.\nSo we can use stack when we want to compare an element with its next element. We can solve the problem in linear time complexity and linear space complexity. Stacks are very useful in questions where we are dealing with piles or related scenarios. Like for example two piles of stones from which we can remove the stone on top to make one of the piles shorter in height. ,1.0
-2142,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are used to store large amount of data.\nStacks are very much important data structure as it has unique property of LIFO.,1.0
-2143,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a modification of array which follow LIFO(Last in first out )property.\nWe use stack data type when we need these operations like in BFS algorithm we need parent node information which is visited lastly so we use stack for this so we can delete that element which is used lastly.,1.0
-2144,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack as a memory to store elements or usually to store large amounts of data,1.0
-2145,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",There are certain problems which can be solved very easily using stacks in place of array because of it works on the principle of LIFO(Last In First Out).,1.0
-2146,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","For secondary or auxiliary storage to store some particular values, \nStack operates on LIFO - Last In, First Out.",1.0
-2147,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are last in first out data structure when we use recursive functions stacks are used by recursion or whenever we need functionality like last in first out we use stack,2.0
-2148,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks is used as a better substitute to solve certain problems in place of using an array,2.0
-2149,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack is used to implement linked lists. In place of arrays , stack is better suited to solve certain problems.",2.0
-2150,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack works on the principle of LAST IN FIRST OUT. when we want to use LIFO type of data structure we use stack,1.0
-2151,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","We use stacks for the cases where we need to solve problem according for LIFO(last in first out ).In other words, we use it where we need to solve the problem by solving for sub problems. We solve the main problem in the last and sub problems firstly.",2.0
-2152,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stacks when we want to access the last element added in the data structure ,i.e. we want to use LIFO principle.",2.5
-2153,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks to store data in a way such that it can leave or enter,2.5
-2154,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","The stack is - last in first out, it mean we are going to fill the stack with data but if we take the data from stack it will give the lasted data which have been inserted in the stack.",2.5
-2155,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stack is used in arrays it used sometimes for sorting ,deleting the index given term and it works like first in last out for this approach it solve easily  ",0.0
-2156,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks to store data in a linear fashion and retrieve data in Last In First Out manner.,2.5
-2157,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","We use stack to store data, it is follows the principle of LIFO; that is last in first out. The element we enters at last should be pop out first.",2.5
-2158,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack in an algorithm whenever we need to have a track of the previous element in the algorithm,2.5
-2159,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Since stacks follow the principle of LIFO which makes insertion very easy in stack .Function by default use stack data structure.,2.5
-2160,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks are used when we need a data structure which requires LIFO(Last in first out) Lets assume there is a problem in which we need values for the last added data in our  query so by using stack it will help us to reduce the time complexity ,2.5
-2161,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-2162,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stack when we have LIFO priority i.e. data that comes last executes first. Example : in our printers.,2.5
-2163,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks in cases where we need to traverse a particular data in a sequential manner and at the same time store the data from that given data in a paricular sequence as per the conditions. For example we us e stack stack to traverse a string and convert it to infix to post fix there we need satck because stack allows first in last out . So we can use the stack to pop or push any values in a sequential manner. ,2.5
-2164,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks because it follows last in first out approach,0.5
-2165,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stack is data structure which uses last in first out approach. it gives data that is  added most latest in the data structure. it has various applications and is used to solve many famous problem such as tower of hanoi . it is also used by operating system to set priority of tasks  ,2.5
-2166,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a data structure that allow its data member to pop out in a LIFO manner which means the last in first out which translates to an opposite of what queue does. ,2.5
-2167,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack are Last In First Out or LIFO data structures. Whatever comes last in the stack will leave first and correspondly with other elements in the stack. They are often used in DFS or depth First search.,2.5
-2168,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-2169,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",We use stacks when we want to always access the last element that went in the stack while performing pop operation. (LIFO),2.5
-2170,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks to store data \nwhen output is required in in lifo manner ,2.5
-2171,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stacks to easy our programing,",0.0
-2172,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",as stacks works on the principle of LIFO (last in first out)\nfor eg:- if you have assign the various trolleys (numbered from 1 to 50) in a mall to the customers coming \nso we can no. the first customer with 50 and next will be assigned with 49 and so on.,2.5
-2173,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","using stacks , we can easily search an element",0.0
-2174,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks works on the principle of FIRST IN LAST OUT ,1.0
-2175,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack for swapping values e3asily.,0.0
-2176,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are use to organize the data in a sequence so that it can be accessed easily \nis stack data is arranged in the form of fifo(first in first out ) \n,0.0
-2177,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks are used when we have to follow the Last In First Out(LIFO) principle with the given data, i.e. when the latest data holds the greater priority to be taken out.\nSome of the uses are:-\n# DFS(Depth First Search)\n# Tower of Hanoi\n# In Prefix, Postfix and Infix problems",2.5
-2178,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks  are linear data structure which  are used to store the data,0.0
-2179,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-2180,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack to arrange the element it flows first in first out policy in which a element which come first will terminate first.,0.0
-2181,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks are the data structures used for storing data and impliment it by following the property of LIFO (last in first out),2.5
-2182,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stack is a type of data structure use to store data ,0.0
-2183,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","because stacks works on last in first out so stacks are useful in graph traversals and other searching techniques. push, pop etc are also used in stacks",2.0
-2184,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stacks to perform the problem in less time interval, their are predefined stl functions, we also use push and pop approach in stack for the functioning of the problem.",1.5
-2185,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",,0.0
-2186,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","Stacks can be used for traversal. Stacks can also be used to store data. It uses the Last In First Out Principle. Stacks are used in recursion, the undo - redo feature, etc .",2.0
-2187,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks is used to store the extra data of some set that is not in use but can be required in further research.,0.0
-2188,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","like  there are some problems we face while working on arrays that is when we want add more items in array we need to preallocate the memory whereas in stacks we need no pre allocation and we can add or remove any item at that instant, ",2.0
-2189,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.","we use stack , to solve the problems in FILO form , like n number of plates are kept in such a manner that the first plate in the last and last plate which inserted in last , chosen to first.",2.0
-2190,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",the stack data structure is used when we need a first in first out type of algorithm for our program.\nthe stack library is easy to understand and use.,0.0
-2191,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stack to store a data in a a database in a ferment  of form of fist in last out it help to optimizer the performance and the result of the given problem ,1.0
-2192,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",Stacks can be use dto ,0.0
-2193,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",stacks provide fist in fist out FIFO approach that way we use stacks ,1.0
-2194,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",last in first out method,1.0
-2195,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",we use stacks for problems which require FIFO and for the prefix and suffix problems too .,0.0
-2196,Why do we use stacks?,"Stacks follows LIFO method and addition and retrieval of a data item takes only Ο(n) time. Stacks are used where we need to access data in the reverse order or their arrival. Stacks are used commonly in recursive function calls, expression parsing, depth first traversal of graphs etc.",the purpose of using stack is to store the data in the present file,0.0
-2197,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueue(addition of elements),dequeue(deletion of elements)",2.5
-2198,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,operations that  can be performed on Queues are \ninsert a node  from end\nremove a node from beginning\nremove a node from middle\nsearch a given node\ndelete a node from beginning \ndelete a node from end,2.5
-2199,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"some common operation which can be perfomed in queues are inserting an element in a queue ,deleting an element from the queue ",1.5
-2200,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"1) Insertion: Inserts at the top of queue\n2) Deletion: Deletes the first element of the queue\n3) finding the topmost element\n4) Searching: Find the topmost element, if not correct delete it and continue",1.0
-2201,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue\ndequeue\nsort\n,2.5
-2202,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"The operations that can be performed on queues are enqueue , dequeue, and to check whether it is empty we can call isEmpty() function. we can also do searching in queues.",2.5
-2203,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enqueue( )\nDequeue( )\npeek( )\nisfull( )\nisnull( ),2.5
-2204,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,. searching an element \n. sorting an element,2.5
-2205,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Following operations can be performed on queue\n- Insertion of node (enqueue)\n- Deletion of node (dequeue)\n- Searching\n- Sorting,2.5
-2206,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,0.0
-2207,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,operations that are performed on queues are as follows-\n1. is empty(); \n2. is full();\n3. insert(); // to insert value  in the queue\n4. delete(); // to delete values from queue,2.5
-2208,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1) top\n2) isempty\n3) merging of queue\n4) pop\n5) push \n,2.5
-2209,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The basic operation of queues is to store data in such form by arranging its minimum and maximum data in according to priority queue.\npop\npush\ndeletion\ninsertion\n,1.0
-2210,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Since Queue works on FIFO- first in first out mechanism we can perform functions on it like PUSH (to enter data) and POP(to remove the data).,2.5
-2211,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,operations that can be performed on queue are :\n- dequeue\n- enqueue\n,2.5
-2212,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue\n- dequeue\n- peek,2.5
-2213,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,queue follows FIFO i.e first in first out\n\noperations that can be performed are:\n1. insert data in queue\n2. retrieve data from the queue,2.5
-2214,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full," (LIFO).\nIn this, if we enter/push the values at the last of the stack then the first value will be returned.",2.5
-2215,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insertion, deletion, extract min ,extract max can be done on queue",1.5
-2216,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push, pop can be performed",2.0
-2217,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Enqueue , Dequeue ",2.5
-2218,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue and dequeue are the very common operations that can be performed on queues,2.5
-2219,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queues are type of data structures which use the mechanism of First In First Out. the operations which can be performed on queues can be \n1.Enqueue: entering particular element in the queue  \n2.Dequeue: deleting the particular element from the queue ,2.5
-2220,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queues are data structures which follow FIFO ( first in first out ). Operations that can be performed on queue are :\ni) push() : inserts data in queue\nii) pop() : deletes data in queue which lies at first position (i.e. inserted first),2.5
-2221,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueue ,dequeue ,traverse , get size , reverse , search ",2.5
-2222,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue: To add an element to the queue.\ndequeue: To delete and element from the queue.\nfirst: To find the first element in the queue.,2.5
-2223,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Insertion, Deletion, Traversal",2.5
-2224,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push, pop and finding the address ( using iterator) of an element in queues.",2.5
-2225,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,there are several operation that can be performed on Queues  :\n1) pushing element into it.\n2) popping element from it.\n3) find address(iterator) of an element of Queues.\netc.\n,2.5
-2226,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"On queues, we can perform post-fix, pre-fix conversions, Breadth First Search.",2.5
-2227,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"pushing, popping are some operations which can be performed on queues",2.5
-2228,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queues :First in first out (FIFO)\nwe can perform various operation on queues like:\n1)enqueue (to insert a element in queue)\n2)dequeue(to delete a element in queue) ,2.5
-2229,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,0.0
-2230,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queues help us to perform certain operations which are pushing and popping a particular element from the queue to get the order and element we want.,2.5
-2231,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"\nOperations that can be performed on queues are \ninsertion, deletion, function call, ",1.5
-2232,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Operations that can be performed on a queue are enqueue (adding an element to the end of the queue) and dequeue (removing the element at the starting of the queue),2.5
-2233,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueqe, dequeue ",2.5
-2234,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The are many operations which can be performed in queue like searching and sorting but the most important one is FIFO (Fist In First Out ) method.\nIn FIFO method the element which inserted first should be deleted first.,2.5
-2235,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full, or \,0.0
-2236,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,we can push an element in the queue on the FIFO principle using push and also pop the last element inserted using pop.,1.0
-2237,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enqueue (Insertion into queue)\nDequeue (Deletion from queue)\nFront (To print the front of the queue),2.5
-2238,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Queues can be used in the cases when we require to use the concept of LIFO (last in first out). For example shortest job first , breadth first search in graphs, level order traversal of trees etc.",2.5
-2239,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Sorting can be performed so amazingly on queues, simple functions like push and pop can be use to sort out an array of the given input by the user.\n",2.5
-2240,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Operation that can be performed on queue are:\n- Dequeue (delete)\n- Enqueue (insert)\n- Disjoint-set operations: -union   -find,2.5
-2241,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,A queue works on the basis of first come first serve and it performs various dynamic operations .,2.5
-2242,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Operations that can be performed on queues are:\n1. Insertion\n2. deletion\n3.rbegin\n4.rend,2.5
-2243,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Various Operations that can be performed on Queues are:-\n1)Push_back\n2)rend\n3)rbegin\n4)begin\n5)end\n5)pop\n6)deletion,1.0
-2244,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Following operations can be performed on queues:\nInsertion\nDeletion\nSelection\nPush back\nEnd\nBegin\nPop,2.5
-2245,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The following operations can be performed on Queues:\n1)Insertion\n2)Deletion\n3)Selection\n4)End\n5)Begin\n6)Pop,2.5
-2246,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Deletion, Selection, Insertion, Pop, Begin",2.5
-2247,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"We can perform various operations on queues like Insertion,Deletion,Union etc.",2.5
-2248,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Major operation performed on queue are\n1. Enqueue - Used to insert data in queue in FIFO order.\n2. Dequeue - Used to remove data from queue. it always removes first element.\n3. Front - used to return first node or first element from queue.,2.5
-2249,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"queues follow FIFO , first in first out \nenqueue and dequeue\nDFS can be performed using Queue",1.0
-2250,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueue, dequeue, insertion and deletion based upon FIFO (First In First Out) technique and searching is also done by removing each element and inserting them back.",2.5
-2251,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,push and pop operations can be performed on queues,2.0
-2252,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Insertion and deletion can be performed in queue. Since in follows FIFO (first in first out). New data is inserted at latest node and data is deleted from earliest node.,2.0
-2253,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,storing of data \nimplementations of stack and various data structures\nfirst go first come type scheme\n,2.0
-2254,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Operations that can be performed on Queues - \n1. Push - inserting an element\n2. Pop - deleting an element\n3. front - getting the top element \n4. end - getting the last inserted element\n5. empty- to find if the queue is empty or not,2.5
-2255,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queues follow the first in first out approach\nQueues have the following operations\n1) insert/enqueue\n2) delete/ dequeue\n3) front \n,2.0
-2256,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1)Insertion\n2)Deletion\n3)Updation-Priority queue,1.5
-2257,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"add or remove an element , compare elements , etc",1.0
-2258,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queue()\nenqueue()\nempty()\nnull()\npeek(),2.5
-2259,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,insertion and pop operation  ,1.0
-2260,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Some of the operations that can be performed on queues are:\n1. insertion\n2. deletion,1.0
-2261,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"The operations that could be performed on queue(FIFO) are push(helps too insert), pop(helps to delete the first position); first(returns the first position data); last( returns the last position data).",2.5
-2262,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Many operations can be performed on queues like insertion of element and deletion of elements and searching and sorting ,1.0
-2263,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Operation that can be perform on Queues are insertion, deletion and searching.",2.5
-2264,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,QUEUE IS FOLLOW THE FIFO THE OPERATIONS ARE PERFORMED IN ARE :-\nINSERTION \nUPDATION \nDELETION,2.5
-2265,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueue , deque, peek, isfull",2.5
-2266,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The following operations can be performed on queues: \n1. Insertion\n2. Deletion,2.5
-2267,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Enqueue, Dequeue, Search, Empty, Reverse are some of the operations.",2.5
-2268,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insert back and pop front, other operations can include iterating the data",1.0
-2269,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1.Insertion \n2. Deletion \nQueue is used to perform breadth first search in trees or graphs.\n,2.5
-2270,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insertion, deletion, searching, ",1.0
-2271,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. reversing\n2 traversing\n3. searching of an element.\n4. sorting,2.5
-2272,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Following operations can be performed on Queues:\n->Enqueue\n->delqueue\n->delete ,2.5
-2273,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Deletion, merging, insertion can be used on queue while priority queue arranges the information with respect to the information provided by the user.",1.0
-2274,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,the operations that can be performed in queues are\n1 push( insert an element)\n2. delete( delete the element from the queue)\n3 emptying of queue,2.5
-2275,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The queue operations are as follows:\n\n1.create\n2.enqueue\n3.dequeue\n4.isEmpty\n5.isFull\n6.size,2.5
-2276,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue (push)\ndequeue (pop)\ntraversal,2.5
-2277,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,queues is used for first in first out and it is uesd for various operation such as BFS and varipous other operation .it is used to treverse on level vise on the graph.,2.5
-2278,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,queue()\ndequeue()\nfront()\nisEmpty()\npush(),2.5
-2279,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queues are FIFO (first in first out).Can be related as to a queue of a movie ticket .push() pop() front() top() enqueue() dequeue(),2.5
-2280,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,basic operations that can be performed on queues are :\nEnqueue() - Insertion of elements to the queue.\nDequeue() - Removal of elements from the queue.\nPeek() - Acquires the data element available at the front node of the queue without deleting it.\nisFull() - Validates if the queue is full.\nand many more.,2.5
-2281,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,operations can be performed on queues are insertion deletion merging searching sorting,2.5
-2282,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue and dequeue,2.5
-2283,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue-add data to queue\ndqueue-delete data from queue\nsearch element \n,2.5
-2284,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Enqueue, Dequeue",2.5
-2285,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Dequeue, Enqueue are the operations can be performed on Queues. ",2.5
-2286,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Operations in queue are- enqueue, dequeue, peek.",2.5
-2287,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"deletion, insertion, sorting ,",2.5
-2288,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queue implements First In First Out(FIFO). \nInserts the element and takes out that element first.,2.5
-2289,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"The operation that can performed on Queues are Push() , Pop() , Top() and etc. ",2.5
-2290,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"queue, dequeue, front",2.5
-2291,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,queues are basically first in first out (FIFO) so it helps in many ways to solve problems ,2.5
-2292,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Operations that can be performed on a queue:-\n1. Enqueue (insertion)\n2. Dequeue (deletion)\n3. Traversal,2.5
-2293,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,push pop enqueue dequeue,2.5
-2294,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Following operations can be performed on queues:\n1- Insertion\n2- Deletion\n3- Searching,2.5
-2295,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Enqueue , Dequeue",2.5
-2296,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"On analysis if n queens problem when observing a queen, we can discard the entire row, and entire column of the 2D array. Further we can eliminate other positions by going from [ i ][ j ] to [ i+1 ][ j+2 ] till the end of the j column and then doing j++. A queen can move vertically, horizontally and diagonally form her position at [ i ][ j ].",2.5
-2297,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,like push refers to adding an element to a queue\npop refers to deleting a top element from a queue.,2.0
-2298,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,FIFO Type Data Structure(First in First Out)\nOperation:\n->pop\n->push,2.0
-2299,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"FILO operations can be performed , to build heap we use queues.",1.0
-2300,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Operations that can be performed on Queues are enqueue i.e., adding an element to the end of the queue , dequeue i.e., remove an element from front of a queue, isempty to check if queue is empty or not.",1.0
-2301,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,push \npop,1.0
-2302,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enqueue() \nDequeue() \nPeek() \n,2.0
-2303,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,q.push - insert elements in queue\nq.pop - delete elements in queue\nq.isempty() - checks if the queue is empty or not  \n,2.0
-2304,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Queue is used to store data and then retrieve it in the same order as placed. We can insert, delete, and search in a queue.",2.0
-2305,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,operations that can be performed on Queues are push and pop.,1.0
-2306,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,pop(): Removes the element from the front\npush_back(): pushes the element at the back\ntop(): return the front most element\nempty(): checks whether the queue is empty or not,2.0
-2307,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push, pop, sort, are the most basic operations that can be performed on queues. push gives us the first entered element. pop deletes the first entered element. Queue works on FIFO: First in first out. ",2.0
-2308,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,top():returns the top element\npop():eliminates the top element\nempty():to check whether the queue is empty or not,1.0
-2309,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Push and Pop operations can be performed on Queues. We can also check if it's empty or not. ,1.0
-2310,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Stacks follow LIFO order. Last in first out.\nThey are used because we can insert and delete elements in them with ease.,1.0
-2311,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. Enqueue - Insertion\n2. Dequeue - Deletion\n3. Traversal \n4. isempty\n5. isfull\n6. find top element,2.0
-2312,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push operation,\npop operation,\nsearch operation",2.0
-2313,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enqueue()\nDequeue() \nPeek()\nisFull() \nisNull()\n,1.0
-2314,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Expressions with operands and operators can be evaluated using a stack. To check whether the parenthesis in an expression match, stacks can be utilised for backtracking. Additionally, it can be used to change one expression into another. It can be applied to methodical memory management.",2.0
-2315,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Some operations that can be applied on the queues are:\n1. Push (to insert elements at the end of the queue)\n2. Pop (to remove elements from the start of the queue)\n3. Seek (Top) operation (to get the first elem of the queue)\n4. Size operation (to get the size of the queue),2.0
-2316,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1) create\n2) enqueue\n3) dequeue\n4) isEmpty\n5) isFull\n6) size,1.0
-2317,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. We can find front element\n2. Rear Element\n3. Pop out elements\n4. Sorting\n5. Searching\n6. Number of elements,1.0
-2318,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"pushback(). top(), pop() and other operations can be used on queue.",2.0
-2319,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,isempty \nsearch \nenqueue\ndequeue,2.0
-2320,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The most common operations on queue are Push and Pop. ,1.0
-2321,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full, or \,2.0
-2322,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,There are many opearions that can be done on Queues. Some of them are :\n1. Insertion\n2. Deletion\n3. Traversal,1.0
-2323,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Extraction of first element present or we can call popping out, Reducing the size of the queue, Sorting on queues are possible, Min-Max elements retrieval, Insertion , Deletion  ",2.0
-2324,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. push operation to add some object in the queue\n2. pop operation  to remove some object from the queue\n3. seek operation returns the object present at the first place in a queue\n4. size operation returns the size of the queue\n5. is_empty operation to check whether the queue is empty or not,1.0
-2325,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Inserting , deleting ,searching , traversing .",2.0
-2326,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Queues are used for their FIFO (First In First Out) properties. The operations that can be performed are pop, push, peek, is empty.",2.0
-2327,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Stack uses LIFO (Last In First Out) principle which means that element occurred last will exit first. It majorly used in reversing the given input and also in some algorithm of Graphs and trees.,1.0
-2328,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"operations which can be performed on queues are:\ncomparison of string , calculating the pattern we want from text,\nqueue ca be used in binary tree for finding complete binary tree",1.0
-2329,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,
-2330,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"create, eequce,  dequce,  size,  is empty, is full.",1.0
-2331,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,
-2332,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"pop()->deletes the very first element in the queue, push(x)-> inserts x at the back of the queue, size()->tells the size of the queue, empty()-> checks whether the queue is empty.",1.0
-2333,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The Operation performed in queues are\n Enqueue (for inserting the element )\nDequeue (for removing the element )\n,1.0
-2334,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,In queues we can perform \na)push()\nb)pop()\nc)delete() \nd)isEmpty() operations,2.0
-2335,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,some operations are:\n1)queue- add element to the queue\n2)dequeue-remove element from the queue,1.0
-2336,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insertion, deletion can be performed on queues",1.0
-2337,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insertion ,deletion",1.0
-2338,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,first in first out operations,1.0
-2339,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push() ->to push the data into the queue  , pop()-> to remove the front element , top() -> to show the value of the front element ",2.0
-2340,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The most common algorithms that can be performed on a Queue are \npush - to insert an element\npop - to get the first inserted element\nisempty - to check if the stack is empty ,1.0
-2341,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1.push:can enter an element\n2.pop: can delete the element at the top\n3.front: can return the top element(which is entered in starting)\n4.size: can return the size of existing queue\n,2.0
-2342,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"We can perform operations like enqueue(insertion in queue) , dequeue(deletion in queue). ",1.0
-2343,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,dequeue : to remove the first element from the queue.\nenqueue : to push an element at the end of the queue.,1.0
-2344,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Operations which can be done on Queue are enque, deque, changing the value of element etc.",1.0
-2345,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueue, dequeue",2.0
-2346,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Operations that can be performed on queues are insertion, deletion , finding the first element of queue using top function,  etc. ",1.0
-2347,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"We can push the elements(enqueue) , pop elements from queue(dequeue). ",1.0
-2348,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue is used to add a new element in the queue.\nDequeue is used to delete the first element in the queue,1.0
-2349,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,queues can perform\ninsertion - push\ndeletion - pop\noperations,1.0
-2350,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queue is FIFO type datastructure.\nThe operations performed on queues-\nPush - Used for pushing or inserting the element in the queue from back.\nPop- Used for popping out an element from the front of the queue.\nTop- Used to get the top  most element from the queue.\nRemove  or delete- For removing a particular element from the queue. For updating the queue.,1.0
-2351,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue() = insertion \ndequeue() = Deletion\ntop()\n,1.0
-2352,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Operations which can be performed are:\nenqueue, dequeue, pop, push, isfull, isempty, delete, sort etc",1.0
-2353,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"we can create min, ,max heap, priority queue, which is works efficiently in searching max or min functions.",1.0
-2354,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queues use the first in first out technique. (FIFO),1.0
-2355,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full," fashion. If we want to store something in a data structure and pop(remove) it in the last, then we use this data structure",1.0
-2356,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Queues can have the following operations-\nFirst in, First out\nSearching\nSorting\n\n  ",1.0
-2357,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insertion,deletion can be perfomed on queues",2.0
-2358,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Operations that can be performed on queues - enqueue, dequeue, isempty()",1.0
-2359,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Pop operation - delete an element from rear\nPush operation - insert an element from front,1.0
-2360,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,pop()- to delete the element\nisempty()- to check is queue is empty or not\npush()- to insert a element on top \n,1.0
-2361,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Insertion, deletion and search.",1.0
-2362,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Push-- To insert element\nPop --- To delete element\nSize---\nisEmpty- To check  Empty Queue,1.0
-2363,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueue , dequeue ",1.0
-2364,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqueue , dequeue ",1.0
-2365,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The operations which can be performed on queues are:\n,1.0
-2366,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,0.0
-2367,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,dequeue()\nenqueue()\npeek()\n,1.0
-2368,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,0.0
-2369,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"operations that can be performed in queue are:\nenqueue(insertion) , dequeue(deletion), front(to get the first element),  back(to get the last element), size(to get the size of a queue), isEmpty(to check whether it is empty or not), etc.",2.0
-2370,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"you can insert, remove and check the front element in a queue, you can also check the size of queue and whether it is empty or not",2.0
-2371,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insert, pop, isempty",2.0
-2372,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,pop- returns and delete the first object or the object that was entered first\nenqueue- insert a new element at the end\nsize()-  return the size of queues\nisempty- checks wether the queue is empty or not\nfront- to get the first element,2.0
-2373,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The operations that can be performed on queues is enque and deque,2.0
-2374,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Queue is structured as an ordered collection of items which are added at one end called rear end, and other end called front end.",2.0
-2375,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. enqueue\n2. dequeue\n3. isempty\n4. isfull\netc\n,2.0
-2376,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Queue supports first in first out (FIFO) property, we can perform :\n1. pop operation -> It gives you first element that was inserted in queue\n2. push -> it pushes a element at last in queue\n3. front -> it gives you the front element present in queue",2.0
-2377,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queue are needed to insert and pop the data which it does in constant time complexity. It follows the FIFO(First first out) fashion.\nInsertion and deletion of the data is very simple in case of queues. There are various other operations that a user can perform by using Queues.,2.0
-2378,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"pop, push and many other.",2.0
-2379,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. enqueue: adding a element at the end of the queue.\n2. dequeue: deleting an element from the front of the queue.\n3. traversal\n,2.0
-2380,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,push-insert an element at the end of the queue\npop-remove an element from the front of the array,2.0
-2381,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Push function\nPop function\nTop function\ndelete function\nare the function performed on queues,2.0
-2382,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,queues are used in first in first out in this data structure first element is get out at last and last element is out at last,2.0
-2383,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Merge sort and quick sort are examples of divide and conquer algorithms ,2.0
-2384,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1)Enqueue()\n2)Dequeue() \n3)Peek() \n4)isEmpty() \n5)isNull() ,2.0
-2385,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Comparing with real life scenarios, like the ticket line, songs playlist.\n\nWe can control as per our preferences (and even the generic preferences - Priority Queue), the order of activities that we want to experience. Some performable operations will be - \n1. Insertion 2. Deletion 3. To know what's at the top 4. To compare the time/other activity related queries if provided with the information.",2.0
-2386,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1) Enqueue\n2) Deque\n3) Front\n4) Insert\n5) pop,2.0
-2387,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Common operation are;(FIFO property)\n1.Insertion from top;\n2.Deletion from top;\n3Retrieval;,2.0
-2388,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The operations that can be performed on queues are:\nEnqueue\nDequeue\nInsert\n,2.0
-2389,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Deletion, Insertion and Retrieval. ",1.0
-2390,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Insert (known as Push)\nDelete (known as Pop - can only be used on the chronologically first entered number)\n,1.0
-2391,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,operations that can be performed on linked list are-;\n1-push( )-which pushes the element in the queue\n2-top( )-to get the top element of the queue\n3-pop( )-to remove the top element from the queue,1.0
-2392,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1)insert\n2)delete\n3)retrieve,1.0
-2393,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue and dequeue are the functions required to make a queue in c++ . Queue follows first in first out. ,1.0
-2394,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,it works on FIRST IN FIRST OUT  principle. we can perform various operations like:\npush()->inserting in queue.\npop()->deleting form queue.\ntop()->gives the top element of queue.,1.0
-2395,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push(),pop(),top() etc.",1.0
-2396,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"pop,push/push_back,sorting,top,etc.",0.0
-2397,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"queue, dequeue",2.0
-2398,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"The operations that can be perform on queues are-push, pop , remove . ",0.0
-2399,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,In the queue operation will be used that the element of array initialize first in last out.,0.0
-2400,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push, pop",0.0
-2401,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Queue is similar to stack but it follows the principle of FIFO; that is first in first out. Many operations can be performed om queue like sorting and searching.,1.0
-2402,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Insertion of element, deletion of an element, swapping between elements, traversing on elements.",1.0
-2403,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enqueue & Dequeue can be performed on queues Priority Queues can also be made. Queues follow FIFO principle.,2.5
-2404,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"queue can perform push,deletion,searching ",0.0
-2405,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue \ndeque are the operation which can be performed on queue,2.5
-2406,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Push and dequeue can be performed on queues.,1.0
-2407,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"The operations that can be performed on queues are-: push, pop, isempty etc.",0.0
-2408,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push, pop, top",0.0
-2409,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. enqueue\n2. dequeue\n3. isfull\n4. isempty,2.5
-2410,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Two common operations that can be performed on a queue includes insertion and deletion.,0.0
-2411,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"push, pop, front are some of the operation that can be performed on Queues.\n\n",0.0
-2412,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,push in queue and pop and top   are perform in queue ,0.0
-2413,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1. Push: push to first location\n2.Pop: pop from first location\n3. Delete: from a particular position.\n,0.0
-2414,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue\ndequeue\n,2.5
-2415,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,0.0
-2416,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"operations on queues are\npush, pop, insert, delete\n",0.0
-2417,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"Insertion , deletion , sorting , searching are the operation ",2.5
-2418,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enqueue and Dequeue are two operations that can be performed on queue.,2.0
-2419,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,enqueue and dequeue,2.0
-2420,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,operation on queues can be performed such as lifo (last in first out ) ,0.5
-2421,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Several operations can be performed on queue like:-\n1. Insert:- To insert a value in queue from the rear side\n2. Delete:- To delete the value from queue from the front side.\n3. Traverse:- To visit the elements and values on queue.\n4. Edit:- To edit the values of queue.\n5. Store:- The values can be stored in queue for later access.\n\nQueue specific operations are:-\n1. Q-In: To insert the value from the rear side.\n2. Q-Out: To delete of push the value out of the queue. ,2.5
-2422,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"enqeue ,deqeue",2.5
-2423,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,there are two operation perfom on Queues:\n--> Enqueues\n-->Dequeues,2.0
-2424,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"in queue we can performed sorting ,searching ,insertion and deletion operations",0.0
-2425,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,insertion \ndeletion\n,2.5
-2426,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enque: Inserting data in Queue\nDeque: Deleting data from Queue,2.0
-2427,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"insert, pop,push",0.0
-2428,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,insertion deletion etc,0.5
-2429,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Enqueue and Dequeue,2.0
-2430,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,The operations that can be performed on Queues are :\nEnqueue : Enter the element in the queue via the end of the queue\nDequeue: Remove the element in the queue via the start of the queue\nisFull : Checks if the queue is full or not\nisEmpty : Checks if the queue is empty or not,2.5
-2431,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Operations that can be performed on Queues is ,0.0
-2432,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"dequeue , enqueue,\ninsert .delete",2.0
-2433,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,we use enqueue  and dequeue ,2.0
-2434,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,Insertion: Queues follow the Last in first out order.,1.0
-2435,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,in queues the method which is used  insert update delete,0.0
-2436,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,,0.0
-2437,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,queues provide fist in last out FILO  approach operation insert delete push pop ,0.0
-2438,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,first in first out.,1.0
-2439,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,1)insertion\n2)deletion\n3)enqueue\n4)dequeue\n5)isempty()\n6)isfull(),2.5
-2440,What operations can be performed on Queues?,The below operations can be performed on a stack – (1) enqueue() − adds an item to rear of the queue (2) dequeue() − removes the item from front of the queue (3) peek() − gives value of front item without removing it (4) isempty() − checks if stack is empty (5) isfull() − checks if stack is full,"sorting, searching, algorithms can be performed on queues",0.0
-2441,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is a basic searching algorithm in whick the element to be searched is stored within a variable and is then compared with every element of the array till the element we are looking for is found.,2.0
-2442,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is when we search for the elements from beginning to end and at whatever location the number is found we return its address  its time complexity is O(n) ,2.5
-2443,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear searching we search for the given element one after the other so the time complexity of linear searching is O(nm) \nit can be useful if we a small array ,1.5
-2444,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear search checks every element of the data structure and checks if the values match, if not, it moves on to the next element",2.0
-2445,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is basic searching algorithm which goes through the entire data structure like an array and returns tthe element if it is found. It checks the entire length of the data structure and hence is less time efficient.,2.0
-2446,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear search is used to search a element in a data structure like array, string in o(n) time complexity. Here we implement a for loop on to traverse the data structure and match its every element to the element we are searching for, if it gets matched ,then break and we get out of loop.",2.5
-2447,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the simplest method of searching a data set.,1.0
-2448,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear search , the algorithm traverse the whole array and if it finds the key element or the desired element then it prints that element.",2.0
-2449,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted."," In Linear searching the program traverse through each and every element in array or node in link list and checks whether the element matches the key or not, if key is found the program returns the element and its position otherwise it displays key not found",2.0
-2450,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Search is defined as a sequential search algorithm that starts at one end and goes through each element of a list until the desired element is found,2.0
-2451,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",this is searching in which values to be searched is to be searches from the first index till the last index until the value is found .It takes o(n) time .,2.5
-2452,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Searching of any thing in a linear time is called linear searching . For instance u have a 1-D array and have to find the element then it will be an example of linear searching .\nint find= 25; , int n ( size od array)= 6;\nfor (int i =0 ; i < n ; i ++)\n{\n     if (a[i]==find)\n{  return i ; }\n}",2.0
-2453,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",This is the searching technique in which we compare the given key one by one in a linear form.,1.5
-2454,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear Search is a form of element traversal and searching in which we start from the initial element and go on checking the next element until we find the desired element in any data structure. Though it takes linear time complexity it is not feasible for large amount and we thus need to resort to other searching techniques like binary search, median search etc. ",2.0
-2455,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a searching technique that uses for loops to search the values .\nits time complexity is : O(n^2),2.0
-2456,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In this searching method we are searching required element from index 1 to last index consicutively until the element is found.,2.0
-2457,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching refers to searching all the elements one-by-one\n\nIt is a brute force search technique and will use the maximum time to finish,2.0
-2458,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",,0.0
-2459,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the manner in which we iteratively traverse the whole array to search the given element present in it.it has complexity of 0(n),0.0
-2460,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is searching whether an element exists in a linear list or not. It is a simple search where we check each element of the list and compare it to the element we have to find. If the element is found then we make the counter=1 otherwise the counter remains 0 as we had intitially initialised it.\n,0.0
-2461,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",When we search for the target in an array or any other data structure by going one by one from one end to the other is known as linear searching. It is a brute force and a very simple approach but it takes more time compared to other efficient algorithms.,2.5
-2462,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","When we search an array  or any other data structure in a specific direction , one by one from one end of it to another , then it is known as linear searching. However linear searching is more time consuming than the other efficient algorithms.",2.0
-2463,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is a way to search for the particular element in a given array. Each element of the array is comparted with the element to be searched when the required element is found it returns the index as well as that element from the array taking O(n) time complexity. ,2.5
-2464,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is a searching algorithm which used to find a \,0.0
-2465,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching take O(n) time to search an element from the data structure. example array take O(n) time to search an element.,1.5
-2466,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",When we search for an element linearly in an array or linked list by traversing one by one through the array or list till we find the element required in O(n) time where n is the size of the array or list.,2.5
-2467,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",The kind of search which takes linear time(O(n)) for searching an element is called linear search.,2.5
-2468,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Searching technique which takes linear time O(n) to find an element in a given sequence/array.,2.5
-2469,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching the data from start to end one by one is called linear search. The time complexity of linear search in O(n). ,2.5
-2470,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","When we traverse an array from the beginning till the end checking each element, this approach is known as linear searching.",2.0
-2471,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the searching in which the particular element is searched linearly that is the first element of the array is checked first and it linearly increases from arr[0] to the size of the array. That's how the element is searched one by one in a linear manner. It has the time complexity of O(n).,2.5
-2472,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching: suppose we are given an array and a element value (key) then we will iterate on every index of an array until we find that key. this will take o(n) time.\n,2.5
-2473,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is basically searching an element in a linear time where searching element say x is compared to every element present in the data structure.,2.0
-2474,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a searching in data structure helps to search a particular element in a given array.\nIt start from the first element and checks the element then second and continue until found the particular element in the array in o(n) time.,2.5
-2475,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In Linear Search we traverse each element of the given array and we run a for loop from starting index of the array to the last index of the array. We start traversing each element and we will stop at that index at which the element which we want to find is equal to the element while traversing the array . And after that we will return that index . And if that element doesn't exist in the array then we will return -1.  ,2.0
-2476,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is a searching technique. In linear search we use key element to compare each element in a given array. ,1.5
-2477,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",It is a searching technique in which we iterate the array and compare each element with key element.,2.0
-2478,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In the Linear Search we will search the element by going on every index.\nWe will implement a temporary variable which will traverse the array and if the element will found at any of the index it will return the index of the array then we will able to found the element through the linear search. ,2.0
-2479,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",(LIFO).,0.0
-2480,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching refers to traversing all the elements one by one resulting in O(n) time complexity.,2.5
-2481,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Searching for an element by traversing in the given data structure element by element is known as linear searching. Its time complexity is O(n), n being the size of the given data structure. ",2.5
-2482,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",A search technique that requires the visiting or analyzing each element while traversing to find the required element is called linear search. Its time complexity is O(n).,2.5
-2483,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a method defined under the searching methods of linear data structure. in this yo go left to right to search for the desired value.,2.0
-2484,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is the most basic search algorithm that we can perform. It has time complexity O(n).\nIn this we set the element that we want is set as \,2.5
-2485,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Comparing an element with the every element of an array from beginning to end is known as linear searching.,2.0
-2486,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search algorithm uses a loop to singly traverse the data and search for a particular key on a specific time. Its time complexity is equal to O(n) where n is the number of elements in worst case.,2.5
-2487,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is the most common searching algorithm used to search a particular value in a array by traversing over each item present in a array.,2.0
-2488,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is a process of checking each and every element of the given data structure with the key element. ,2.0
-2489,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is the algorithm in which we search for an element in an array by looking at every element in the array from start to finish.,2.0
-2490,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching means to search a particular element in a single line or at a same level from the start to the end.,2.0
-2491,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is defined as method of finding the element by traversing through the data structure one after the other and matching it by each element linearly. ,2.0
-2492,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is a type of searching technique in which system traverse on each and every element in order to search a particular item.,2.0
-2493,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Searching involving the time complexity of O(n),1.0
-2494,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",a linear search algorithm is where we traverse through an entire row of elements and compare each of them separately to check for the element we need to search for.,2.0
-2495,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is the method simply find the target element in list of values provided with n2 time complexity using only for loops,1.5
-2496,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is the simplest form of searching in which we search for an entity in a given set of data one-by-one in a linearly order.,2.0
-2497,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is the brute force method to search a particular number or character in a given input of list or array respectively,2.0
-2498,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear searching is searching by using one iterator. \nLinear searching can be done in arrays, linked list, hashing, etx.",2.0
-2499,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching includes going at every element in a list from the start and searching for the element\nIt take O(n) time where n is the number of elements in the list,2.5
-2500,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is the process of searching using linear time complextiy ie. Big O of n.,2.5
-2501,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",to search for an element linearly in all elements ,1.0
-2502,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is just simply moving right of element is greater and left if lesser and finds the element this way,1.0
-2503,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear Search is defined as a sequential search algorithm that starts at one end and goes through each element of a list until the desired element is found, otherwise the search continues till the end of the data set. It is the easiest searching algorithm.",2.0
-2504,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","linear searching is the easiest method of searching. Starting from the beginning of the data set, each data is matched from the key (element to be found) until a match is found.",2.0
-2505,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",When we start searching from the first element of data set and keep searching each element linearly one by one until the element is found.,2.0
-2506,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear search is the simplest form of searching where you can directly compare the given element to the next element and reorder according to their sizes,1.0
-2507,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a type of searching where we search our info linearly i.e by iteratively visiting the each node.,2.0
-2508,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In the linear searching we traverse a array one to one  if the element is find than stop and print the element  and is time compexity is O(n).\n,2.5
-2509,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is the most basic type of searching in it we search for the element by checking them one by one in an array,2.0
-2510,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear searching refers to searching for a particular data in a linear fashion, i.e, going from one data to another as they are stored in an array.",2.0
-2511,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear searching, the element to be searched is compared sequentially with each of the elements in array. Its time complexity is O(n).",2.5
-2512,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","linear searching is searching sequentially one after another, time complexity o(n)",2.5
-2513,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",When we search for an element by traversing the array. \nIt takes linear time .,2.0
-2514,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","linear searching is one of the sorting techniques in which we declare an array and then compare each element of the array with the key or element we are searching for.\nas soon as the key is found, we print the key along with the position in the array where it was found.",2.0
-2515,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",When we search the element linearly and check them element which we want to search with every element of the data structure from first element to last and when we find the element we can return true.,2.0
-2516,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching as the name suggests is the process of searching elements linearly generally it has O(n) time complexity. ,2.5
-2517,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is when the array is searched in ascending order of its index value in a unidirectional manner.,2.0
-2518,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the searching of a data structure in a linear fashion by comparing the element that we have to find with each element of the data structure(array) and if we find the element we return true and note the position of the element. ,2.0
-2519,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","A linear search is the simplest approach to search for an element in a data set. It examines each element until it finds a match, starts from the beginning of the data set, until the end. The search is finished and terminated once the element is located. If it finds no match, the program gets terminated",2.0
-2520,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",traversal of the data structure while matching it with required item/data is known as linear searching. we can use loops for the same ,2.0
-2521,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",this is the algorithm  which search the element in ,1.0
-2522,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","It is a searching method that searches a given array of elements linearly, that is from the first memory block to the last, OR\nfrom i[0] to i[n - 1]",2.0
-2523,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear searching is searching algorithm with linear time complexity such as linear search,it wll view every element till the required element is not found.Its worst case time complexity is n.",2.0
-2524,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is a searching technique in which we check each element of the list in a linear order until the desired element is obtained.,2.0
-2525,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear search is searching from start checking one by one at a time whether the no is found or not,1.5
-2526,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a searching technique that takes O(N) of time complexity.It starts from first index and compares every element with the required element till it finds the element.On finding the element it returns it index if not found return -1.,2.5
-2527,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is a searching technique that involves visiting each element individually and is efficient in case of unsorted data.,2.5
-2528,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In Linear Searching it compare each number with every other number and then gives the output. \nIt has time complexity of O(n) times.,2.5
-2529,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the searching of the element one by one. ,1.0
-2530,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",A sequential search is made over all items one by one is known as linear searching.,1.5
-2531,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","in linear searching, we compare every element of the array to the element which is to be found.",2.0
-2532,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is traversing the array in a linear fashion. \nStart searching from the first element and go on searching until you find it.,2.0
-2533,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is  a algorithm in which we perform our searching a array element one by one if the element of our search present in the array . We return the element from our search.,2.0
-2534,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",a searching method which used to search a data in a liner form it has no special condtions it will just check every element which is data structure.,2.0
-2535,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear search we compare (if it is equal or not ) the element we have to search with every other element present in the array and return the index if we find the element or else return -1,2.0
-2536,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Searching in a linear time is called linear searching.\nLinear searching is an example of this type of searching. It has a linear time complexity [O(n)].,2.5
-2537,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching up the elements one by one linearly pointer starts from the start moved and looks at every index wherever it matches it stops ,2.0
-2538,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is searching a data item through simple looping method i.e. searching the entire array for the particular data item.,2.0
-2539,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",We compare each element of the array one by one with the element we want to find if its equal then we found the element . Its time complexity is O(n)\nfor(int i=0;i<n;i++)\nif(arr[i]==x)\ncout<<\,2.5
-2540,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is searching through an entire array or other data structure by observing one element at a time. We observe the element at a[ i ] and if it is not equal to the target we move on to the immediate next element till the end of the array is reached. If it is equal to the target then we return the index found.,2.5
-2541,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",to search for an element by traverse a data structure linearly using linearly loops\nlike in the case of the arrays and linked list we used to search an element simply\nby using for loop not by applying any kind of algorithms like binary search.,1.0
-2542,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is meant by iterating one by one in entire data structure.\n Generally Time Complexity of such algorithms are O(entire length) .,2.0
-2543,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching data within an array linearly is called linear searching.,2.0
-2544,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear searching is the searching techniques which is used to search and array linearly i.e., one by one and time complexity is O(n).",1.0
-2545,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","searching elements in the linear data set without performing any operation like diving it into two half or any other, until the data is found..\nbasically we search until the data is found.",1.0
-2546,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","A linear search is the  method of searching a data set. Start from the beginning of the data set, each element of data is checked until a same match is found .and when the item is found, the search ends. ",2.0
-2547,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",The key to be searched is being constantly compared with all other elements in array using single iterator which is used to access all elements of the array.\nTime complexity is O(n).,1.0
-2548,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear searching we take the first element and then compare it with the desired value, if not same then perform the same operation with the next element till the desired output is obtained.",1.0
-2549,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",the searching which is done linearly in a single loop.,2.0
-2550,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In linear searching we iterate over all the elements until our wanted element is found.,1.0
-2551,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching it the iterative searching where the element to be searched is compared with each element of the array (or the particular data structure used) one by one.,2.0
-2552,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in this searching we traverse to the complete length of the array and checks whether the key matches with that particular index or not,1.0
-2553,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching allows a loop to be run throughout the data set and comparing it with the value being searched for. The loop finally breaks after it finds the position at which the desired value is located. ,2.0
-2554,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",pop() and pusback() and many more.,2.0
-2555,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Searching through all the values of the array one by one from start to end. \narr={1,2,4,5} \nlinear search for 4 will go through each and every element ie 1 then 2 then 4 to find 4.",2.0
-2556,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is a type of searching where nodes are checked one by one in a linear fashion.,2.0
-2557,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",When a search is carried out starting from the first element to the last element,1.0
-2558,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","enqueue , dequeue , isFull , isNull",1.0
-2559,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the simplest type of searching where the items are traversed from start to finish until the element we are searching for is found.\nTime complexity: O(n),1.0
-2560,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is a sequential searching algorithm where we start from one end and check every element of the list until the desired element is found. It is the simplest searching algorithm,2.0
-2561,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching involves searching an element linearly means while traversing an array only once. It has linear time complexity - O(n).,2.0
-2562,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the process of searching by iterating each and every element.,2.0
-2563,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In Linear searching a loop traverse through the whole array till the desired position of the element is reached . ,1.0
-2564,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is the process of searching for an element while iterating through the data structure elements one by one in a linear manner.,2.0
-2565,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",,
-2566,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",This is the most basic type of searching in which each and every element of the data structure is traversed one by one linearly in order to find the desired element.,2.0
-2567,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a search technique in which the elements are traversed one after the other and it takes O(n) time and O(n) space complexity.,1.0
-2568,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear searching each object in a list or array in checked from starting to a particular value if the value of the object is equal to the value we are searching for searching is stopped and index at which object is present is returned or it continue to search from next object onwards for the value until either it finds the object or reaches the end of the list or array.,1.0
-2569,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching by traversing each element of data structure linearly is call linear searching.,2.0
-2570,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a searching algo with O(n) time complexity in which elements are elements are searched in increasing/decreasing order of their indices.,1.0
-2571,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Queues Works on FIFO (First In First Out) which means element occuring first will exit first. It is used in BFS traversal algorithm for graphs and Level-Order-Traversal in trees.,2.0
-2572,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","it use brute force concept that is searching and comparing for every element,  linear searching refers to binary search in which we first divide the string in two half part for finding the key and as we check that is the key lies left or right to the two half string and after according to that we shift mid value to mid-1 or mid +1 ",2.0
-2573,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",traversing in increments or decrements linearly one by one until we find the required element. \nthe search function has a time constant of n as it goes to every n elements each.,2.0
-2574,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",,
-2575,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching means to perform search operation in a linear data structure ,1.0
-2576,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear searching is when we fully iterate over a data structure searching for a specific element in it. It has O(n)(linear) time complexity where n is the number of elements in the data structure, i.e. to search an element linearly the time it would take in worst case is proportional to the number of elements in the data structure.",1.0
-2577,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a type of searching in we search element one by one means we use brute force approach to search an element its time complexity is O(n)^2 .we search one element of an array with the every element of the other array.,2.0
-2578,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching of the algorithm is defined as looking for the element in the serial order from starting of the array . It is completed in O(n) Time complexity,2.0
-2579,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is when we want to search for an element in an array and we check for every element one at a time until we find the element,1.0
-2580,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is searching for the required data in an array in one traversal only,2.0
-2581,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear search each element of the array is checked until a match is found and if not found \,2.0
-2582,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching element in  time complexity O(n) ,2.0
-2583,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching we traverse data structure index by index to find the element ,1.0
-2584,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is the most basic search algorithm for any array. It works the most naive and brute force approach of travelling the data in a linear manner that is from the first element to the last and if at any place we find that the item we are searching for is present in the array then we print that index to mark the position where it is found. ,1.0
-2585,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",the simplest method of searching the data set. We traverse through the whole array/LL till we get the desired output(iteratively);,2.0
-2586,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is simply searching the element while  traversing the data structure from the start to the end one by one.,2.0
-2587,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",the most basic type of searching algorithm where we traverse through all the elements of the data structure until we got the result.\n,2.0
-2588,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Taking example of an array . Linear search is to search for lets say an element \,2.0
-2589,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching along the array by comparing each element with the desired element,2.0
-2590,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear searching , the elements are arranged in an array and are traversed in one traversal only. ",2.0
-2591,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a searching technique in which we simply traverse our data structure and try to match our key with every element in the data structure. If match found means that key is present in our data structure otherwise not present.,2.0
-2592,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is traversing an array or a vector from the first element to the last element in a single turn.,2.0
-2593,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is the type of searching where we shift linearly element by element until the goal element is found.,2.0
-2594,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching algorithm is used to find out an element from the given data structure like an array or an liked list.\nLinear search traverse the target from starting to end linearly but the given constraint is that whenever we find the target value we stop doing searching and return the index or address of that corresponding location where our target is present.\nIf the array contains repeated target element then we can traverse the entire array from start to end simultaneously storing the index in another array.,2.0
-2595,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is a type of searching technique in which we take element and compare it with every other element to put it in its right place and then repeat the loop for every other element\n  ,2.0
-2596,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear searching is an approach for searching an element in an array in O(n) time complexity. Where the cursor goes through the entire list in n time and gives the index where the element is found, if not found in the list it gives -1.",2.0
-2597,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is a process of finding an element by checking each and every element present in the array.,2.0
-2598,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear search, the search occurs in a linear manner from the beginning to the end. ",2.0
-2599,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",1. pop\n2. push\n3. top\n4. isempty,2.0
-2600,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching is a search technique which searches from start to end in a linear manner.,2.0
-2601,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it stores at one end and goes through each element in a list.,2.0
-2602,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear searching is a searching technique in which we iterate from the beginning element by element till the end, stopping if we find the key element we are looking for. If not then we return showing the user the element to be searched doesn't exist. ",2.0
-2603,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is when all the elements of the array are traversed to find the target element.\nfor(i=0;i<array.length();i++)\n{\nif(array[i] == x)\nreturn i;\n},2.0
-2604,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear search the data structure are searched linearly from 0 to n.,2.0
-2605,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In linear searching we search for the key element/target element in an array by traversing the array with the help of loop.,2.0
-2606,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In Linear Searching, We traverse through out the whole data set from 0-n one by one to find the desired element",2.0
-2607,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear searching we traverse one by one through the whole array to find the particular element in searching. since it searches one by one it is less efficient to binary search.,2.0
-2608,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching for elements in a linearly fashion in a data structure with the possible time complexity possible for a program  ,2.0
-2609,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","linear searching is the basic searching technique , it searches the data linearly and ",2.0
-2610,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear seaching is a searching algo use to search a particular element from the given array by comparing the elments one by one.,2.0
-2611,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear Search is defined as a search algorithm that starts at one end and traverse each element of a list until the desired element is obtained.,2.0
-2612,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",A linear searching is a searching in which we search any algo one by one according to question ,2.0
-2613,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is basically a brutte force technique to search an element. Here we use two for loop with the help of which we search an element in array in O(n square) complexity.,2.0
-2614,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching technique in which you go on searching from the first or last element till the end or starting of data structure until you find the target,2.0
-2615,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in this we go to every data element and check whether it matches the input or not.,2.0
-2616,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","in liner searching we search for the element we want in a list(array, vector etc) by going one element at a time from starting to end. It is a brute force technique to find an element",2.0
-2617,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is a type of search in which data is searched from 1st element to the last element  or visa versa . For example in array an in linear search a data is searched from 1st index to last index . and in every iteration element is searched then the pointer moves to the next element.,2.0
-2618,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is to search the data in linear form like in array.,2.0
-2619,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","linear search is basically , traversing the whole array or list and check/compare  for the key element as you traverse the array or list .\nif key element is found we return it otherwise we return -1\nit does the full operation whether or not the element is found",2.0
-2620,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a technique to search lets say a element in an array it checks each element one by one and compares with the target element. The worst case time complexity of this is O(n).,2.0
-2621,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",It is a searching technique which finds the given number in constant time complexity. It the most preferable searching technique because the time complexity is very less and it is the first and oldest searching technique which is taught.,2.0
-2622,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a searching in which we travel at every element to find out the element.,2.0
-2623,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear search , each element in the array right from the beginning is compared to the element we're looking for. When found, it returns the index of that element.",2.0
-2624,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching for an element in an data structure from beginning to end till we found the required element\ntime complexity-> o(n) where the n is the length of the array,2.0
-2625,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is to search the target at every index of array one by one\nits time complexity is O(n),2.0
-2626,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is which in which we search linearly and only one for loop is used and its time complexity is O(n).\n,2.0
-2627,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Stacks work in the way of FIFO (First in first out ) method. It is a data structure which is used to efficiently manipulate data in a way such that the element on the top of the stack goes out and when output is displayed or accessed and there when we input any element in the stack, it goes on top of it. Any algorithm or problem which benefits from the same uses stack.",2.0
-2628,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Traversing the whole data structure and searching whether the required element in the data structure or not.,2.0
-2629,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","When we are provided with data, for example in the form of an array. And we need to search for an element in the array of datas. So we can traverse throughout the array and look for the data. We can maintain a flag variable to mark the existence of the data. We do this in linear time because in the worst case we will have to traverse through all the data provided. This is linear time searching. ",2.0
-2630,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is searching technique/algorithm in which our algorithm traverse through our data single time.,2.0
-2631,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching a searching technique for array/linked list which is used to find inputted element by traversing  the array/linked list one by one from starting till element is found. Its complexity is O(n). ,2.0
-2632,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",The search that can be completed in linear time is linear searching.,2.0
-2633,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",It is a type of search in which each we take the element to be searched and it is being compared to all the elements present in the array. ,2.0
-2634,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Searching elements in a data structure or algorithm where all the elements being searched at the time are at the same level.\nExample: Level-based search in Binary Search Trees.,2.0
-2635,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a technique in which we go to every possible position one by one in a data structure and check it if it matches the given target it is done in O(n) complexity,2.0
-2636,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching an element of an array and comparing them with all the elements present in the array,2.0
-2637,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear search involves checking each element of the array with the input . A for loop is run and an if condition is put inside it to check if the required value is in the array. The time complexity is n.\narr[n];\ncin>> x;\nfor(int i=0; i<n; i++)\n{  if(x==arr[i])\n   {  cout<<\,2.0
-2638,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear searching we traverse all the element in the given data structure . it is a brute force approach. if size of data structure is n than its time complexity is O(n).,2.0
-2639,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a type of brute force searching technique in which we search the desired element for visiting each and every element stored. Time complexity is O(n),2.0
-2640,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","it is a type of a brute force searching technique in which we match a particular element from each element of the vector/array,right from the beginning ,till we have found that element.",2.5
-2641,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear searching, we are given an element that we need to find. We can take all elements as an array. Then we search that element in the array. We will do something like : if arr[i]==x(element that we need) return i; suppose we find the element at i-th position, then return i.",2.5
-2642,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",In linear searching to find the element we have to traversal every element one by one.,2.5
-2643,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",It is the method of searching the element in one d array by traversing the array with the staring index to last index.,2.5
-2644,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",when the array is traversed in a linear fashion and each element is compared to the element to be searched until it is found.,2.5
-2645,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","A searching where we move only in one direction. For example, we can not search depth and breadth at the same time ",2.0
-2646,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",method of searching an element via brute force approach i.e. by traversing each element  and comparing it one by one till we find the desired element,2.5
-2647,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear Searching means iterating the full length of the data structure to find the target value time of linear search is always O(n).,2.5
-2648,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is a type of searching in which we traverse linearly to search an element ,2.5
-2649,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it starts from one end and go to the end point in one direction we have given a array data structure in  that linear search work like it starts from position 0 to end position without going back  ,2.5
-2650,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Simply searching through all the elements in a data structure starting from first element to the last element is called linear searching.,2.5
-2651,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear search is an algorithm to find a specific element in an array in most simplified manner . The time complexity of linear search algorithm is O(n). As at max it can find the element at the last index. Everey time it traverse the array it matches the value with the value that needs to be find and returns whenever it comes true. ,2.5
-2652,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a searching process in which we traverse a ptr while comparing each item \nit has a order of n^2,2.5
-2653,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in this every index (data) of the data structure is one by one compared by the desired element. it has time complexity of O(n).,2.5
-2654,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is the type of searching in which no condition to simplify or reduce the number of object to search is applied. It searches according to the command in whole of the object.,1.0
-2655,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Traversing and comparing each element of the provided array with the key element is called linear searching. It takes O(N) time complexity.,2.5
-2656,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching all element in data structure in a simply in which we traverse all element one by one .,2.5
-2657,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching refers to searching each element in the array one by one from the first element till you find the target element.,2.5
-2658,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear search is when an item is searched from a list linearly one by one i.e. start to the end\ntime complexity ; 0(n^2),2.5
-2659,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is searching that starts at one end and goes to each element in list until all element are searched ,2.5
-2660,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","suppose we are given a array \nso, linear searching means running a loop and and traversing through the array with a time complexity of O(n)",2.0
-2661,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear Search , we traverse the whole array and compare each element of the array with the element to be searched . The time complexity of linear search is O(n);",2.5
-2662,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Searching the element in an array by comparing it one by one to the element in the array staring from position 1 is known as linear search.,2.5
-2663,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear search is defined as the searching  is limited to a small text or in small problems.,0.0
-2664,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear search time complexity is o(n) to search a element in a array it search each index for that element in the given array ,2.0
-2665,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching is the searching of the desired element in the given data by traversing all the elements of the data one by one.,2.5
-2666,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear search is searching for a element in list by running loop and compare every element with element we have to find,2.5
-2667,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",,0.0
-2668,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in linear searching the search the element in ,0.0
-2669,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is a searching technique used in case of linear data structure,1.0
-2670,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","In linear searching, we search the whole data from starting until we found our data, it consumes huge time in comparison of Binary Search\nWe perform linear search on unsorted data. ",2.5
-2671,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is a type of searching in which searching is done from start and the element is searched linearly until the given value matches the element in array.\nfor example in an array of 10 elements we start from index 0 and traverse until we match the required element.,2.5
-2672,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","the element are being searched one at a time, sequencly, unlike in binear searching.",2.5
-2673,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",,0.0
-2674,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","Linear Searching is an algorithm to search an element in a linear fashion.\nThis means that we would start checking the elements from the start , move forward one step at a time.\nIf the element is found , then we can return the index.\nElse we can return -1 if we do not find the target element\nIt takes O(n) time complexity to perform",2.5
-2675,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",Linear searching define as the searching of an element in an array by traversing it in one direction of get required element.,2.5
-2676,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",in this we start from the first element of array and continue to look the given element to be searched throughout the array and return 1 if it is present else return 0.,2.5
-2677,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.","the searching of  element in such a way that , we have to take comparison of first two element first and then take comparison of  it and third element .",2.0
-2678,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching traverses through the array and compared every element with the element that needs to be found until it is found.,2.5
-2679,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear search is the searching technique which help in searching a data in an system and give an output ,2.5
-2680,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",searching for the given ,0.0
-2681,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching is one by one check all elements and providing answer,2.5
-2682,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",we use linear search to find the data in array.,2.5
-2683,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",it is searching algorithm in which in which we find a element or key by travsering through each element one by one ,2.5
-2684,What is linear searching?,"Linear search tries to find an item in a sequentially arranged data type. These sequentially arranged data items known as array or list, are accessible in incrementing memory location. Linear search compares expected data item with each of data items in list or array. The average case time complexity of linear search is Ο(n) and worst case complexity is Ο(n2). Data in target arrays/lists need not to be sorted.",linear searching the the searching technique in which we just compare and traverse the array ,2.5
-2685,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion and selection sort are different in their working principles. insertion sort uses a reference element and then sorts other elements in relation to the reference element.,2.0
-2686,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list", insertion sort and selection sorts are different in a way that in  insertion  sort we insert  an element and compare the values with other nodes and swap the values time complexity can be O(n^2) but selection sort time complexity is O(n),2.0
-2687,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",the main diffrence between insertion and selection sort as in insertion sort the elements are already in sorted oreder,1.0
-2688,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Selection sort selects an element and traverse to find the least value smaller than it\nInsertion sort inserts the element at its suitable position in the array,2.5
-2689,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort picks each of the number and places it in the correct position in the current array.\n\nSelection sort picks the element and places on basis of the number itself.,2.0
-2690,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort takes one element and traverse every place of that array and tries to find best place for that element and place it.\nSelection sort takes one element and compare it with its next and place at its right place .\nThere are max no. of swaps in selection than insertion.,2.5
-2691,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2692,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort has less time complexity than selection sort.,1.0
-2693,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",They have different time complexities,1.0
-2694,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2695,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Both are sorting techniques and they both are different as in  insertion sort  we divide the array in two parts one the sorted one and other unsorted and we assume that the first element is already sorted and from the unsorted array we try to put that element in correct sequence in the sorted array and in selection sort we sort the elements from beginning as we find the minimum in this and with each complete iteration of j loop one element is sorted .,2.5
-2696,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort -  it check the element and insertion it to the next position if found greater than next one \nselection sort :- it selects the element from the array and sort it by the end . ,1.0
-2697,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort  we  insert the given key and compare it with others and arrange the given array in sorted form.\nIn selection sort we select any element and assume it  primary key and then comparing each element of the array with that primary key if it lesser than it then arranging the array in sorted form.,2.5
-2698,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Insertion sort is not an inplace sorting techniques, it requires external storage. Eg: the first element is stored separately and then the next element is compared and based on whether it is smaller or greater its place is decided. Its like arranging cards in two hands.\nSelection sort is inplace sorting technique in which the minimum element is searched and then swapped with its new position.    ",2.5
-2699,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in selection sort the element is place dat the index I  after every iteration where as \nin insertion sort the traversal is done for the entire range of (0-n) in every iteration.,1.0
-2700,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In Insertion sort we find the correct position of ith element in the range (0 - i) positions.\n\nIn selection sort we find the smallest element than place it to the first position and so on. ,2.0
-2701,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort is a basic brute force technique where you find the smallest element in each iteration and place it in the first position in the array and maximum iterations is the size of the array\n\nSelection sort is also a basic brute force technique but here we find the largest element in each iteration and place it in the last of the array,2.0
-2702,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Linear searching is a kind of searching in which all the elements are searched 1-1.\n,0.0
-2703,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort is much like placing the element on the required position just like placing in card by moving the rest of the elements forward to create space for it whereas\nselection sort is putting the biggest the biggest elements on the last position in each step they have bit higher complexity of o(n square).,2.0
-2704,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2705,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sort we select an element and compare it one by one  with every other element succeeding it for sorting the array whereas in insertion sort,1.0
-2706,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort we select an element and we compare it with every other element present , and thus succeeding in sorting it",1.0
-2707,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2708,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list"," element in an array. It is a simple algorithm in which we traverse entire array and check at every position if that element at that position is equal to key. Since we have to compare every element present in array to the key, time complexity of linear search is O(n). Here, n is the size of array. ",0.0
-2709,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Both  insertion sort and selection sorts  take O(n^2) time t sort an array in average and worst case . but the difference is this \ninsertion :  \n\nselection: take a values till particular index and sort them by comperisions  and repeat it till array size -1 ,1.0
-2710,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion Sort is when an element is inserted at its correct position while sorting the array.\nSelection sort is when ,1.0
-2711,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sorting the chosen element is placed at its correct position in the array while in selection sorting the smallest element is selected and placed in the sorted part of the array. Insertion sort inserts the element in its position and selection sort selects the desired element and places it in the correct position of the sorted array,2.5
-2712,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort the element chosen is placed at its correct position in the array while in selection sort the smaller element is placed in the sorted part of the array.\nAlso, in insertion sort the element is placed at the position it belongs to and selection sort selects the position accordingly and place it at the correct position on repeatedly swapping the elements.",2.5
-2713,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sort we the minimum element from the array and and put it at first position. now excluding the first element we do the same process for the remaining array until whole array get traversed and sorted. \nBut in selection sort we start prom first element pick it and compare it with other element  starting form the 0th index. if we get an element smaller than it we move to next index and as soon as we get an element larger than chosen element  we place the chosen element before it and perform the same for the remaning element until it get sorted.,2.5
-2714,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort, the index of the smallest element is found in the array and it is swapped with the first element of the array.  Second smallest is swapped with the second element. This is done until the entire array is sorted(reached the last position).\nIn insertion sort, an element is taken and is inserted in its correct position in the array.",2.5
-2715,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in selection sort we start from the first element, take it and compare it with all the other elements, as soon as we get the greater number than the chosen element , we leave the element whereas in insertion sort we continue to sort the further array.",2.0
-2716,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","insertion sort:: (references : playing cards) here, we first pick a single element and after that we apply two condition\n1) if upcoming element is larger then we will place it in right side\n2) if upcoming element is lesser then we will place it in left side\n\nselection sort:: in selection sort we use two for loop and in every iteration of a outer loop we find the minimum element with the help of inner for loop and place that value in starting position(corresponding to I value) of the array with the help of swapping. \n\n\n\n",2.5
-2717,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in selection sort minimum element is found and placed at starting of array .\nin insertion sort elements are swapped as soon as the small element is found the sort is like arranging deck of cards .,2.5
-2718,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort helps user to insert the number in the sequence and stop as soon as the number is greater than the input number whereas in selection sorts it keeps on going till the final number.,2.0
-2719,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Insertion Sort- firstly we initialize any no.  as min. element from the given array . Then we go to the starting index of the array if that element is lesser than the min. element then swap(min. element , element) and if not then we will move to next index  we go on repeating this process till the last-1 index of the array.\nIt takes O(n) time. \nSelection Sort- It takes O(n^2) time. \n Last index gets sorted first and first index get sorted at the last .  ",2.0
-2720,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",The difference between insertion and selection sort is ,0.0
-2721,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we pick the element and insert it in its right position whereas in selection sort we select the element to placed at a particular position.,1.0
-2722,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort we will insert the element at a specific index position of the array. Whereas in the selection sort we will first traverse the array, place the elements in the correct position by swap . The Time and Space complexities are also different. ",1.0
-2723,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Operations that can be performed on queues are:\n1. Searching.\n2. Sorting.\n3. Deletion.\n4. Insertion.\n,0.0
-2724,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","insertion sort is sort of opposite to selection sort. In insertion sort, we treat the first element as already sorted and then add the rest elements one by one such that each element is added at the corrected spot such that the resulting array is already sorted. at nth turn, first n elements are sorted.",1.0
-2725,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, elements are inserted one by one in a sorted array, and placed at their correct position (beginning with first element, which is sorted in itself).\nIn selection sort, it traverses to each element and then searches for element smaller than it in the array ahead of it, if found swaps with it and eventually this sorting occurs from last to first.",2.5
-2726,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Insertion sort works by picking an element and storing it in a temporary variable and shifting all the elements smaller that the element before it and then pushing the element in the gap created due to shifting of elements,.\nWhile selection sort selects and element and searches for an element smaller to it from the next index , when found , it swaps both the elements and continues the same for all other elements.",2.5
-2727,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort you create an another array to store the sorted values but in selection sort whole operation of sorting the array is performed on the same array.,1.0
-2728,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list"," variable, then the whole array is traversed until any element gets matched with key. If no element is found, it returns NO solution but for that it needs to check with each and every element of the Array. Hence, the worst time complexity is O(n), where n is the no. of elements in the array.",0.0
-2729,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort ,an element is put between two elements of an array while in selection sort ,the element is put ,based on the array comparison.",1.0
-2730,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort and selection sort are different as insertion sort follows the in-place algorithms while selection sort does not.   ,2.0
-2731,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion Sort and selection Sort are different as in selection sort the elements are sorted by comparing with minimum element present in array while in insertion sort the element are sorted based on previous element present in whole array.,2.5
-2732,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sorting after finding the largest or smallest element we insert a suitable function. Whereas in insertion sorting after finding the smallest or largest element we make a swap within the array.,2.0
-2733,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort we select the smallest or largest element in the array and swap it with the element in the starting position.\nWhereas, in insertion sort we select the smallest or largest element and insert in the appropriate location.",2.5
-2734,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sort we select the smallest or largest element from the array and swap it with the starting element whereas in insertion sort we insert a element in the array and swap it with other elements until the array becomes sorted.,2.5
-2735,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort and selection sort are different when based on their time complexity. ,1.0
-2736,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion - \nselection - ,0.0
-2737,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort has a time complexity of O(nlogn) whereas Selection sort is of O(n^2),1.0
-2738,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In select sort we have to find the maximum element and accordingly have to put it in the end of the array but in insertion sort we have to take an element and compare it with other element until we find a larger element of which then we have to compare that larger element instead.\nSo the main difference is in insertion we compare all the element with the element in hand but in selection we find the required element and place it on the place we need it to be on the final array.,2.0
-2739,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we find the smallest or largest element and insert it in the front or back and keep doing this for other elements until list is sorted while in selection sort we just select random elements and swap them with biggest or smallest elements,2.0
-2740,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2741,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",both defies different worst case time complexities ,0.0
-2742,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort helps you to sort the data as soon as each element is inserted in the array on the spot. It inserts element as soon as it is entered to its correct position.\nSelection sort takes input all at one and them perform operation to sort the data. It takes first element and then swap it with its correct position and so on till the whole array is sorted. ,1.5
-2743,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort, we take minimum (or maximum) each time and put it in the right position in the list\nIn insertion sort, we check select an element, and then compare its value with every element of the list. This process is repeated for every element except the last",2.0
-2744,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sort we use a selected value and then compare with other values to sort where as in insertion sort there is no selected value.\nTime Complexity of Insertion Sort=O(n log n)\nTime Complexity of Selection Sort=O(n * n),2.0
-2745,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","insert - o(n) , elements are inserted during traversal and list is sorted\nselection - o(n^2) , it is done by finding max or min element first and then all elements are sorted",2.0
-2746,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",selection sort searches the unsorted part to find the minimum element while insertion ,0.0
-2747,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort we have to insert one space in steps during sorting but not in selection sort.\n,1.0
-2748,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we take an element and insert it in its right location by traversing through the data.\nIn selection sort the smallest element is selected and placed at it right place and the same process is repeated throughout.,2.5
-2749,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","THE insertion sort inserts the values in a presorted file to sort a set of values. on the other hand, the selection sort finds the minimum number from the list and sorts it in the required order.",2.5
-2750,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort and selection sorts are 2 types of sorting techniques,1.0
-2751,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort and selection sorts are different because in insertion sort we first insert the element and while inserting the next element we check whether its smaller than the already inserted element or not while in selection sort we first select the minimum element then we keep on inserting like that.,2.5
-2752,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort  the  array is also divided into two parts a sorted part and an unsorted part.  In selection sort, the input array is divided into two parts a sorted part and an unsorted part. Selection sort scan the unsorted part to find the minimum element, while insertion sort scan the sorted part to find the correct position to place the element.\n\n\n\n",2.5
-2753,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort compares element to determine the position of element in a partially sorted array whereas selection sort compares the element to find the minimum element in an unsorted array ,2.5
-2754,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Insertion Sort sorts the element while it is being inserted into an array. Selection sort goes through an array in a linear fashion, sorting two adjacent elements until they are in their sorted position.",1.0
-2755,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, there is shifting of elements to insert the element at its particular place whereas in selection sort, there is swapping of elements.",1.0
-2756,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2757,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, we assume we have 2 sub arrays : one containing the lone element is considered to be sorted and the other sub array is the unsorted one. We take an element from the unsorted array and place it in the sorted array in such a way that the previously sorted array remains sorted.\nIn selection sort, we select the first element of the array and sort it with the rest of the array by swapping with the smallest element in the array. Then after coming out of the loop the second element is compared with the rest and so on.",2.5
-2758,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Selection sort: Compares elements to find the minimum element in the unsorted part and places that element in the sorted part. \nInsertion sort: Compares elements to determine the position of an element in the partially sorted array.,2.5
-2759,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2760,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort we start iteration from 1 index and check previous one if its greater ,then start inserting/moving it right-words.\nwhere as in selection sort we start iteration from 0 index then start comparing consecutive elements if not rightly placed then swapping them .",0.0
-2761,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort inserts a value in the array and arranges the rest with respect to that particular value while selection sort compares each particular value to sort all of the values.,1.0
-2762,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in selection sort we choose a particular element and we find its correct position by comparing it with all the elements and then placing it at the correct position\nin insertion sort we insert the particular element at a place and then move it comparing it to the elements next to it,1.0
-2763,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",The main difference between insertion sort and selection sort is that insertion sort performs sorting by exchanging an element at a time with the partially sorted array while selection sort performs sorting by selecting the smallest element from the remaining elements and exchanging it with the element in the correct location.,2.5
-2764,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2765,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",inserction in which each elemement is palced in the cporrect place and in selection on the base of max amd imn we place the element ,2.0
-2766,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort, we take an element and put it in it's correct position\nIn insertion sort, every element that comes in the array, we put at the correct position as it comes",1.0
-2767,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort and selection sort differs in term of stability insertion sort requires extra space where as selection sort does not require the extra space,1.0
-2768,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","The insertion sort inserts the values in a presorted file to sort a set of values. On the other hand, the selection sort finds the minimum number from the list and sort it in some order.",2.0
-2769,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in selection sort we 1st take a number let it be a smallest number and iterate through the loop if any smallest number is found than swap with the number \ninsertion sort is taking minimum element and adding the number on left side if small and on the right is large,2.0
-2770,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we place the element to its right position but in selection sort we find the min element compare it and then make suitable swaps,2.0
-2771,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",selection sort finds the smallest element then places it in first position and so on  however insertion sort ,1.0
-2772,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In Insertion Sort we compare all the elements and place them at their correct position.\nIn Selection Sort we find max and min and then compare it with the elements in the array and then swap it.,2.0
-2773,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort inserts the value in a presorted files to sort a set of values.\nSelection sort find the minimum number from the list and sorted in some order.  ,2.0
-2774,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort inserts the value in a presorted file to sort a set of values.\nSelection sort find the minimum number from the list and sort it in some order.,2.0
-2775,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in selection sort, we compare every element with every other element and find minimum element, which is replaced by first element. This sort algorithm works until array is sorted.\nin insertion sort, we compare elements before the selected element and swap them so that they are sorted.  ",2.5
-2776,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort has a better time complexity than the selection sort. \nAlso selection sort selects the least element and replaces it with the other.\nInsertion sort creates an array of sorted elements and inserts the element in the sorted array.,2.5
-2777,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2778,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",selections picks minimum element first and insertion picks a element and put it in its correct place,2.5
-2779,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort with the helps of two loops we compare every element with every other element present in the array whereas in selection sort we take first two elements and and compare them and then set their sorted position in this way we do with the rest of the element in the array.\nthere are difference in terms of complexities selection sort is slightly better than insertion,2.0
-2780,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sort a minimum element is selected and is compared with other elements to sort.\nSelection sort works by comparing adjacent elements,2.5
-2781,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",sorts are basically the sorting techniques means sorting to get the output easily and effectively\ninsertion sorts means sorting up the given data in an order and then inserting up the element which needs to be added and element that has to be added is too done in an order\nwhereas as selection sort means you are given up the data that has to be performed certain operations on it you will sort and then select sorting makes things easier,2.0
-2782,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2783,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2784,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort and selection sort are different in the sense that in selection sort we select the element and run two loops. In the first loop we iterate through the array if there is any element in the array smaller than the selected element we swap else we continue. In the outer loop if after all the swapping takes place for the [ i ]th element we move on and repeat the same steps for the [ i+1]th element till the end of the array.\n,1.0
-2785,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",selection sort works on minimum number of swaps possible,1.0
-2786,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Selection Sort is different from Insertion Sort\nin selection sort we sort our array by finding largest element one by one \nand sorting array in Length*length no of operations\nbut insertion we try to do it by key value,1.0
-2787,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","both the sorting approaches have different techniques , selection sort sort first two indexes and moves linearly whereas insertion sort inserts a value and checks it with every index.",2.0
-2788,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Selection sorts scans the unsorted part to find the minimum element while insertion sort scans the sorted part to find the correct position to insert.,1.0
-2789,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",we insert particular element on its correct position while in selection we insert element on particular space.,2.0
-2790,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",The insertion sort inserts the values in a presorted order to sort the values.\n The selection sort finds the minimum number from the list and sort it in some order,1.0
-2791,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort element is inserted between the array elements after comparison.\nIn selection sort element is selected on by one and placed in another sorted  array.,2.0
-2792,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we compare and place the minimum element at the start and then perform the same operation the the next elements.,2.0
-2793,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort, an element is picked and then placed in its correct position and in selection sort, smallest element is found in each loop and is placed in the start accordingly.",2.0
-2794,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Insertion sort: in insertion sort we start from 1st index and compare it with previous index and swap if the previous element is larger. Then we move on to 2nd index and compare it with the previous 2 elements and sap if required. Iteration continues until the array is sorted.\n\nSelection Sort: In selection sort, we take the smallest element and iterate over the remaining elements and update smallest if required. If intial smallest and updated one is now same then we swap. The loop continues till the array is sorted.",1.0
-2795,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sort the minimum is selected and pushed to the sorted part of the array. In Insertion sort a duplicate value is copied and then replaced with the appropriate value to be inserted. The time complexities are also different.  ,2.0
-2796,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in selection sort -we consider the first element as sorted and selects the smallest element from the rest array and put it at the starting.\nin insertion sort- we consider the first element as sorted and picks the element form the rest array and insert it at the right index depending on the pattern of sorting.,2.0
-2797,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In Insertion sort, it proceeds from the very first value and inserts the value after thorough calculation at it's right place. Then it moves onto the other value and repeats the process. Meanwhile in Selection sort, the picked out value is chosen on the basis of a criteria (Max at times) and after proper comparison places it at the right index. And then moves on to the next clarified value.",1.0
-2798,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Searching the required element one by one in the array is called linear searching.\nComplexity is  O(n).,1.0
-2799,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,
-2800,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","insertion sort pushes the element after checking each element in position  but it is not fixed position and can vary but in selection sort once the element is checked and selected, the position will not vary.",2.0
-2801,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort searches the unsorted part to insert the element at right position whereas selection sort finds the minimum element from the unsorted part,1.0
-2802,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In linear searching the array or  vector is traversed using a loop in which each element of the array is at least visited one time until the loop ends. It's complexity is of O(n) where n is the number of elements in the array. ,2.0
-2803,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort a sorted_array is maintained and elements are inserted in such a way where we have to find the min elem (for index 0) and then insert the next greater or equal element than the previous n-1 element in the sorted_array for subsequent indices. In selection sort the elements are swapped in-place until the array is sorted. ,1.0
-2804,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Selection sort has a time complexity of O(n^2) in all cases.\nInsertion sort has a time complexity of O(n^2),1.0
-2805,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort, we take the first element as minimum element, then we find the minimum element from the remaining array using a for loop and then compare both elements and place them accordingly.\n\nIn insertion sort, we bring the smallest element from the array and insert it in it's correct position.",1.0
-2806,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",selections sort is basically sorting elements by checking all elements. Insertion  sort gives the right position of the  maximum element and then works on the rest of the array.,1.0
-2807,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,
-2808,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, a element is inserted at its correct position, while in selection sort, an element is selected for its correct position. ",1.0
-2809,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","push, pop (it is basically operation \",1.0
-2810,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,
-2811,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",The insertion sort inserts the values in an already sorted structure to sort a set of values.  The selection sort finds the minimum number from the structure and sort it in some order.,2.0
-2812,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort an object is take and taken into the list so that the list remain sorted while in selection sort list is sorted according to places like at 0 for index for an array the smallest element is searched through various comparisons using for loop and swapped at 0th index with index at which it was present then same process will happen for 1st index and second smallest value is placed at it and process continues till all objects are in their places(basically end of the list or array) and list or array is sorted.,1.0
-2813,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort take O(n^2) time while selection sort take O(n) time,2.0
-2814,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort and selection sorts both are sorting algo which have O(n^2) time complexity but there working is different.,1.0
-2815,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Linear Searching is the algorithm used to seach the target element by visiting every element in the given array.\nTime Complexity- O(n),1.0
-2816,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","insertion sort works like inserting the element one by one in array or string or stack or queue and then sorting according but it is not in case of selection sort , in selection sort we first sort elements according to requirement and then select only require elements means selecting elements according require , by doing this we reduce the time complexity to some extend ",2.0
-2817,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","selection sort selects the element and takes it as the max/min  and then moves to the next element and compares it top the previous \nmax/min and places it, it selects every element and picks them up and puts them in the order like this. Where as insertion sort uses\nmore time than selection sort in particular cases and it inputs the elements as min\\max.",1.0
-2818,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,
-2819,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,
-2820,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we keep inserting an element into the sorted part of the array but in selection sort we select an element and then put it at its correct place in the array.,2.0
-2821,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we check for each element in the array and insert the element if the element is greater then its preceding element  and in selection sort,1.0
-2822,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In selection sort the array is divide to into two parts one is from 0 to i and other is from i to j . In this we assume that the array 0 to i is sorted in desired manner and we increase the i and thus sorts the whole array,2.0
-2823,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort, we select the smallest element from the array and put it in the first index of the array and then find the second smallest element and so on.\nin insertion sort, we first sort the subarray containing first and second array ,and when that is sorted we insert the third element in the position in which the subarray containing first ,second, third element is sorted and so on.",1.0
-2824,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort each and every element in unsorted portion of the array is compared with each and every element in sorted portion of the array and then swapping takes place whereas in selection sort we traverse an array the number of elements present in the array times and then minimum element is found out in each traversal and swapping takes place,1.0
-2825,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list", or -1 is displayed which brings its worst case complexity of n.,2.0
-2826,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort first the position is identified where element is to be inserted and then sorted comparing with next element\nin selection sort the initial element is separated and rest of the elements are compared and so on,2.0
-2827,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort we divide array into two parts , 1st element and 2nd to last element in second  , we one by one traversse \n ",2.0
-2828,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort is like sorting a deck of cards we are holding in our hand. We start with the first element and assume that it is sorted and then we start putting all the other elements in the correct places in the sorted array. By the time we reach the end of our array we have a sorted array. \n\nSelection sort woks on the principle of finding the smallest or largest element in the array and then putting it at the starting or end respectively. \n\n,1.0
-2829,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","The insertion sort inserts the values in a presorted file to sort a set of values.On the other hand, selection sort finds the minimum number from the list and sort it in some order.",1.0
-2830,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort ,we simple the first element of the data structure to be sorted and we start our sorting from the second element by comparing it with the first while in selection sort we select the first element and compare it with coming elements and do the swaps as per our need.",2.0
-2831,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In Insertion sort we first sort first two elements and then place or insert the next element in that sorted part at the correct place until the every elements are sorted.\n\nIn selection sort we select the first  element and compare it with all the elements and find min and place it in the beginning and then we select another are do the same until the last element has selected and placed.,2.0
-2832,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list"," at each and every position of the array and if value of any position == a, then we need to return it. This is what called linear searching. ",1.0
-2833,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort : first element is key and the next element is checked while it is smaller than key based on that pushes it towards the front of the array.\nselection sort : this technique is based on swapping principle wherein it linearly picks one element and searches the smallest element in the entire array.\nselection sort checks the entire array always but in insertion sort only the key and the next element is checked hence in the best case the complexity of the insertion sort turns out to be O(n) while in selection sort it is O(n^2),2.0
-2834,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort , the elements are inserted at the start and then positioned according to the increasing or decreasing order. While in selection sort , the entire array is traversed and checks for its position. Both have different time complexities. ",1.0
-2835,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, we simply divide our data structure in two parts one is sorted and second unsorted. And then add first element in sorted one and assume that it is sorted and then we traverse our data structure and update the the two parts on traversing . It is like how we arrange playing cards. \n\nIn selection sort, we select one element and compare it with every element present in data structure and place it at its desired position. Then take another and repeat the process until the array is sorted.\n",1.0
-2836,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we take the first element as key and check with the next element and keep on repeating. while in selection sort we take the first element and check it with the whole array to decide its position in the array and keep on repeating the process.,1.0
-2837,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort takes a new blank array and then places the elements in sorted order in the array. It picks up the array elements of the given array one after the other in the given order.\nselection sort on selects the elements of the given array and sort them in the given array itself.,1.0
-2838,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort is the sorting algorithm which uses the card method of sorting in which means put the element at its correct location with simultaneously using the swap.\nSelection sort is the sorting algorithm which finds the minimum element in the entire array and then insert it to the first position and we simultaneously increse the first index and search minimum again in the array available after the position and when the end is reached then the algorithm stops.,1.0
-2839,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort you take a element and compare it with all the elements occurring before it in the array and place it accordingly in the array and repeat if for rest of the elements\nin selection sort you select a element and place it at specific position it should have in sorted array,1.0
-2840,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",They are different based on their worst case time complexity.,1.0
-2841,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort it divides the array and then place the first element on the right place in first array, and then increasing the size of first array it keeps on repeating the same process until it reach the end of array, however in selection sort , it will find the lowest or highest element and replace it by first element then the pointer keeps on moving to the next array finding the suitable element and swapping it in the end and at last we get the sorted array.",1.0
-2842,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion Sort has a pivot while selection sort does not have a pivot. ,1.0
-2843,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Linear searching is a way of searching in an array which takes O(N) time. In this type of searching, we iterate the array and check whether the required element is present in the array or not. If the element is present, it returns 1 else it returns 0(if boolean function of linear searching is made)",1.0
-2844,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort and selection sort works differently as in one chooses a pivot and sorts accordingly while the other doesn't.,1.0
-2845,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort have time complexity of O(nlogn) whereas selection sort has time complexity of O(n),1.0
-2846,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort when we find the element greater let us say B from an element A then we insert B right after A shifting the rest of the array by one place making room for B. In selection sort we swap instead. So the difference is in the swapping. ,1.0
-2847,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, the smaller elements are moved to the left and the bigger elements to the right.\nTime complexity = O(n) in best case\nIn selection sort, each element is compared with the rest of the elements in the list/array and the smallest is brought to the front.\nTime complexity= O(n^2) in best case",1.0
-2848,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in selection sort we find the exact position of the data and in ins,1.0
-2849,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In inser,0.0
-2850,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In Insertion sort, the element is picked and placed as per desired neighbor set\nWhere as, In Selection sort, we find the exact place of the selected data",1.0
-2851,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In the selection sort we find the min. number from the given array of list and sort them in order while in the insertion sort  we insert the values in an array and save it in another array to sort a set of values. ,1.0
-2852,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","selection sort uses the approach of divide and conquer on the problem statement to solve it as parts and later combine them later when the task is complete , while insertion sort works in a different manner.",1.0
-2853,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",As the name suggests insertion helps in inserting the given data and then sorting accordingly unlike selection sort helps in choosing the data we require to find,1.0
-2854,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we use to insert the data and directly arrang them in the sort way. And in selection sort we use to sort the particular data that is already inserted. ,1.0
-2855,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","insertion sort inserts values in a already sorted file to sort a set of values. whereas, selection sort finds the minimum number from the list and sort it in a given order. ",1.0
-2856,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2857,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2858,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort you put every element at its correct position and in selection sort you find the next min or max element in rest of the array and swap it with the current element,1.0
-2859,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, we take an element from an array and insert it in the correct position of an array and it is a linear search. in this we divide array into sorted and unsorted and take the first element and sort them into the unsorted array\nIn selection sort, we divide array into sorted and unsorted array and pick an element and put it into the sorted array into the correct position. it is a divide and conquer approach",1.0
-2860,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in selection sort we use two for loops to find the position of a element one by one, whereas in insertion sort we can exchange two continous element depending on which is smaller, and we keep on doing this until the array is sorted",1.0
-2861,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2862,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Insertion sort and selection sort are two popular sorting algorithms, and their main difference lies in how they select and place elements in a sorted sequence manner.",1.0
-2863,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",basic difference between the two is thst selection sort is a linear sort which runs on comparing each element with one predefined minimum or maximum element whereas insertion sort works on divide and conquer technique.,1.0
-2864,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Selection sort is a technique in which we find out the smallest element in whole array and swap it with element present at 1st position and so on till the end, so it takes O(n^2) time, whereas insertion sort is a technique in which you assume that only first element is present first and for second element you shift according to first element by comparing whether it is smaller or big just like sort a pack of cards by picking card one by one it's time complexity is also O(n^2).",1.0
-2865,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","time complexity of insertion sort is O(n*n) as two for loops are used in it. It takes the element which is present in the first index than it compare with other elements than perform the swap operation at each index.\nwhereas Selection sort compares the two adjacent element and perform the swap operations , it also has the time complexity of O(n*n).",1.0
-2866,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we insert the element in correct position iteratively and in selection sort we comapre to element to place a element in correct position iteratively.,1.0
-2867,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort, the input array is divided into two parts: a sorted part and an unsorted part.\nThe algorithm repeatedly finds the minimum element in the unsorted part and swaps it with the leftmost element of the unsorted part, thus expanding the sorted part by one element.\nSelection sort has a time complexity of O(n^2) in all cases.\nInsertion Sort:\nIn insertion sort, the input array is also divided into two parts: a sorted part and an unsorted part.\nThe algorithm picks up an element and places it at its correct position in the sorted part as soon as it is added.",1.0
-2868,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we pick the min element and put it in front of our array and repeat the process for remaining\n\nin selection sort we pick the adjacent elements and compare them and swap if first element is bigger than the second than we perform swap operation on them,1.0
-2869,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we find the minimum element and place it to its correct position and decrease the size of traversing array\nIn selection sort we find the maximum element and place it to its correct position and decrease the size of traversing array,1.0
-2870,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we basically insert elements in the starting by removing elements from the last and applying the sorting algorithm \nand in selection sort we basically make the adjacent swaps and apply the sorting algorithm,1.0
-2871,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","Queues work in the way of LIFO (Last in first out ) method. It is a data structure which is used to efficiently manipulate data in a way such that the element on the top of the stack goes out and when output is displayed or accessed and there when we input any element in the stack, it goes on last of it. Any algorithm or problem which benefits from the same uses Queues. \nOperations like accessing the top element in the data structure, removing it , manipulating/changing it, inserting elements on the end of it etc. can be performed. Pop, push etc.",1.0
-2872,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","The insertion sort inserts the values in a presorted file to sort a set of values. On the other hand, the selection sort finds the minimum number from the list and sort it in some order.",1.0
-2873,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In selection sort. We traverse through the array O(n2) times. In each traversal we make the smallest/largest data to reach the end of the array by performing swapping action. This way, we sort.\nIn insertion sort. We assume that the first element is the smallest/largest element. And then we compare it with the upcoming elements, performing swapping wherever and whenever necessary. The worst case for this sort is also O(n2), but the best case can be linear time as well.",1.0
-2874,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In Insertion sort we pick an array element and insert it by comparing it with all other elements of that array.\nIn selection sort we keep comparing each element with its next element and if it smaller than we placed it in the starting index of the array and do this again for the next elements.,1.0
-2875,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In Insertion sort, a element is selected and is placed at its right position it is done till all elements are at their correct position  for eg. arranging deck of card\nwhereas in selection sort  we sort element ",1.0
-2876,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort,an array is traversed ",1.0
-2877,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we select one element and perform certain operations on it till it gets to the right position and this process if performed on all the elements.\n,1.0
-2878,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2879,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort elements are inserted at their correct position just like we arrange playing cards in our hands \nwhereas , in selection sort we pick one element and place it at its correct position.",1.0
-2880,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2881,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",<< i << \,0.0
-2882,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we place the element at starting position by comparing and swapping it with other elements and repeat the process to do sorting.\nin selection sort we select an element and place it at its correct position and repeat the process to do sorting.,1.0
-2883,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion and selection sort are different in manner of sorting method they use. In insertion sort a single  element is inserted first and then other elements are inserted according to their momentarily correct position by comparing with elements already inserted. While in selection sort we take out an element and place it in it's correct position and repeat the process accordingly.,1.0
-2884,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort is a type of sorting in which we take an element from the array then we find then we insert it at it's correct position. In selection sort we pick an element and then find it's correct position and then interchange it. In selection sort we run a nested loop which the outer loop runs from start to end of the array and the inner loop runs from i th to n-1 th position of the array,which is not the case in insertion sort.",2.5
-2885,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, we start by taking the first two elements of the data structure and sort it, then we take first three elements and sort it, and so on ,till the last element is reached.\nIn selection sort, we set the first element as minimum element , then we compare it with next element, and if the next element is smaller then these two will be swapped and it will become the new minimum element. this process will go on till all sorting is done.  ",1.0
-2886,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2887,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in the insertion sort the sorting the array with taking the two index and making a for loop and interchange the value with the other one which ore shorter. while in selection sort the sorting the algorithm with one index and finding the sorter tern of array with the help of loop and select it and take it as a first index .\nAlso both have different time complexity.,1.0
-2888,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort, we need to insert the existing elements to a new array, in such a manner that the new array is sorted at each step.\nin selection sort, we select the smallest element in the array and then swap it with the first element, then we look for second smallest and swap it with second element and so on.",2.5
-2889,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we select a pivot point which keeps on changing while in selection sort we select a middle element and keeps comparing the other elements from it.,0.0
-2890,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort we insert the new element in the already sorted part of the array one by one till the complete array is sorted whereas in selection sort ,1.5
-2891,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort swaps the element with the  next element if the value of the next element is smaller and swaps many times in a single loop.\nBut is case of selection sort the minimum element is selected in single loop and after one loop is over the element is swapped.,1.0
-2892,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort divides and insert the given value while selection sort selects the value which is required,2.5
-2893,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we sort the array in ascending order \nwhile in selection sort we sort the array in descending order\ninsertion sort time complexity o(logn)\nselection sort o(n),0.0
-2894,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort compares one element to every other elemt and swaps if value is greater or smaller.,0.0
-2895,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",In insertion sort one pointer is place at the starting index and other pointer is placed at the next index and swapping is done by comparing the other index element with elements within that particular bracket of first index and the other idex which increases as every time the otherv pointer shifts to the right. On the other hand selection sort is done by placing apointer at the first index and other pointer at the last index and swapping is done by comparing the two elements till the time both the pointers meet or cross each other.,1.0
-2896,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2897,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort  a kind of new auxillary  data structure is created and then data is inserted one by one where as in selection sort data is selected on different indices. they also have different time complexities.,2.5
-2898,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2899,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort assumes that initially there is no element present in the array and we add elements one by one and compare it with the previous one that are being added whereas in case of selection sort,1.5
-2900,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2901,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, swapping is used to sort the array.\nIn selection sort, minimum element(of reamaining array) is found out each time and put at the starting index.",1.0
-2902,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort selected item is compared to the next item in iteration\nin selection sort two items in the list are compared if prev is greater than the next then we swap,1.0
-2903,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2904,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort:- traversing the array and putting the smallest element on the first position and so on\nselection sort:- selection the smallest in the pairs of 2 i.e running 2 loops i and j one starting with 0th position and other from 1st,1.0
-2905,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In Insertion sort , at first we traverse the whole array and find the 1st minimum element and place in it correct place , and we find the second minimum element place it in correct place and we do until the whole array is sorted.\nIn selection sort , we take two pointers and compare them , if the first element is bigger we swap it otherwise we increment the pointer. we do it until awhole array is sorted",2.5
-2906,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion Sort and Selection sort are different in terms of their time complex-cities. Selection Sort is the most efficient and less time consuming in terms of solving aproblem.,0.0
-2907,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we insert the value one by one by comparing not only one but also with another one. selection sort is the sort which used sort by comparisons,0.0
-2908,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in ,0.0
-2909,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort keeps on inserting the right elements one by one in the data structure while selection sort selects the data and compares it to the elements and then put it accordingly.,2.5
-2910,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2911,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2912,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion  sort just compare the values and insert the values and selection sort is similar as quick sort,2.0
-2913,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort is used when the array is completely sorted where as selection sort is used when the array is partially sorted,0.0
-2914,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",,0.0
-2915,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","selection sort is a sorting technique in which we select an element and compare it to the array, till it reaches the correct position we do it with every element in an array.\ninsertion sort we create another array and sort the elements.",0.0
-2916,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort element is being inserted at the end and in selection sort element inserted at random. ,0.0
-2917,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we insert data in the given array according to question\nand in selection sort we select the data which is mentioned in the ques from the given array,0.0
-2918,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","In insertion sort, we select the element in the unsorted part of the array and then position it at the correct position in the sorted part of the array.\nIn selection sort, we find the minimum or maximum element in the array, swap it and then again repeat the process until the array is sorted",2.5
-2919,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",Insertion sort and selection sort are different by the operation they performed as selection sort is used to select the element from an array and insertion sort is used to insert an element in an array. ,2.0
-2920,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we assign pre value to max and min and then change them accordingly after comparison\nselection sort have more time complexity,0.0
-2921,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort we insert element first and arrange in right manner , but in selection sort we first select the elements and then sort in right way",2.0
-2922,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","insertion sort is faster than selection sort\nin selection sort, the digits are compared with each other and then swapped\nin insertion sort a formula is used.\n",0.0
-2923,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion short and select short are different because we give in put to both of them by the different method ,0.0
-2924,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in terms of time complexity,\ninsertion sort words better than selection for smaller frequency values\nand selection works better for larger freq of values\nbut it is seggested to use selection sort",0.0
-2925,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",insertion sort break the array then assemble in sort and in selection sort in we select in element and compare and sort them,0.0
-2926,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",in insertion sort we can insert the value & when the array is partially not defined in selection sort.,0.0
-2927,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list",the number of comparisions in selection sort is less than insertion sort ,0.0
-2928,How insertion sort and selection sorts are different?,"Both sorting techniques maintains two sub-lists, sorted and unsorted and both take one element at a time and places it into sorted sub-list. Insertion sort works on the current element in hand and places it in the sorted array at appropriate location maintaining the properties of insertion sort. Whereas, selection sort searches the minimum from the list","in insertion sort, we just compare the value to the last of the array value and interchange if needed, like we just compare , traverse and edit the array, \nin selection sort, we divide the array into two equal parts and start comparing using a pivot element in the given array.",0.0
-2929,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort uses a partition index where where all the elements on the left side of the partition are smaller than the partition elemnt, this process is repeated till the array is sorted. The partition element is chosen accordingly.",2.5
-2930,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort firstly we pick one pivot element generally the first one and then compare it with the neighbour of it if the element which is found is less than the pivot element we swap the values and likewise we compare it with other elements and if the element found is greater than the pivot element  then we don't swipe and finally after performing this at end we get a sorted array,2.0
-2931,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,if we have an array of n elements so first we point l to the first element of the array and h to the last element of the array then we take the mid by l+h/2 \nif mid=desired element exit we found the element if mid is less than the desired element then we will make h=new mid and then we will find the mid again till we find the element \nif mid is greater than the element we will make l=mid and then recusivly this will go on  till we find the element,2.0
-2932,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort is a divide and conquer element, it takes a random pivot element and compares it with other elements and then swaps there positions",2.0
-2933,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is a divide and conquer algorithm. It starts by picking a pivot element. Then it compares the elements with the pivot and swaps the posititons. It uses this pivot to divide the list or array and then recursively calls itself for each  divided part.,2.0
-2934,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort is used to sort an array in logn time complexity. \nFirstly here we tried to find a pivot element , it may be first , last , middle or may be a random element .\nThen we have two pointer one at first and last element of array then we compare elements to pivot element and tried to place elements smaller than pivot before than pivot and larger than pivot till we starting pointer comes to end and end pointer comes to start. After that we got a sorted array.",2.5
-2935,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,a quick sort  is one of the most efficient sorting algorithm. it works by breaking the array into smaller ones and swapping the smaller ones depending on comparison with the pivot element picked. ,2.5
-2936,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quicksort\n. Pick an pivot element \n. Divide the array in three parts.\n. Left side if the elements are less than pivot element.\n. Element equal to pivot element.\n.Right side if elements are more than pivot element.\n.Then apply quicksort on the three subparts.,2.0
-2937,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,-> Quicksort firstly picks up an pivot element from the list \n-> Then is divides the list into three sub lists 1. element lower than pivot element 2. elements equal to pivot element 3. elements greater than pivot elements\n-> Then it does the same for th,2.0
-2938,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort is a Divide and Conquer algorithm. It picks an element as a pivot and partitions the given array around the picked pivot,1.0
-2939,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"It works on divide and conqueror approach whole array is divided by choosing an element as pivot and then elements less than pivot are in one array and element grater than or equal to pivot are on other side ,when only 2 elements are present in array after dividing ,using the Partition function we sort the array and solve the smaller part of the question so that to get the solution of whole problem.",2.5
-2940,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort 1st divide the array and traverse it from both the side if it found the element  greater then from there is  again breaks the array and do it till the heaviest element comes at start and lowest at end and then merge the array again , basically it uses divide and conquer approach .",2.5
-2941,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on divide and conquer method , first it divide the given array in two and then further dividing it until it consist two values which are then sorted the given array and then again adding together to form the sorted array.",2.0
-2942,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick Sort is based on divide and conquer mechanism , we take a pivot element and two pointers on either end of the array. The pointer on the start (p ) moves towards the left and the one of the end (q) moves towards the right while comparing the values to that of the pivot element. Conditions of pivot  element being greater than value of q or less than q are checked for the pointer to move forward and if the pointers cross each other then the element is swapped with the pivot and this continues till the entire array is sorted.",2.5
-2943,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works on the basis of divide and conquer methodology.,1.0
-2944,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"We randomly pick a pivot element \nWe have two pointers front at 0th index, back at (n-1)th index, front pointer will increase and back pointer will decrease.\nElement smaller than pivot element will be placed to the left and element bigger then the pivot element will be placed at right side.\nWhen front > back , front and pivot will swap the element.\nSame procedure will be repeated for left side and right side of the pivot element until array is sorted.",2.5
-2945,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by creating a partition on an element in the array such that all the elements on the left of the partition are smaller than the partition element and all the elements on the right side are greater than the partition element,2.5
-2946,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"The only difference between insertion sort and selection sort is that of the selecting item.\nIn selection sort, one of the very beginning item is selected and it is compared with all the rest of the items ahead.",0.0
-2947,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort finds the partition element in the array first or the pivot element mostly the first element.\nthen it place the pivot to its correct position and we passes two recurrences from 0 to partition -1 and partition +1 to last element of the array.\nin each iteration of recurrence all the pivots are places to its correct position thus the array become sorted. partition places to its correct position by shifting the element smaller than it to left and bigger on right.\n\n\n,2.0
-2948,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick Sort is a sorting technique used to sort a list given to us.\nIn quick sort we make a function partition(). We start traversing the list given from the start as well as the end using the variables say, 'start' and 'end' respectively.\nThese two variables act as starting and ending indexes of the values respectively in the list.\nWe also choose anyone element of the list say the starting element and store it in a variable say 'part'.\nWe compare the starting element and ending element with the element stored in variable 'part'. If the starting element is greater than 'part', then we move the start index forward. If the ending element is smaller than part then we move the end index backwards. This way we do until the start and end indexes cross each other, as soon as it does we replace the element at the end index with the element stored in variable 'part'. We proceed in this way till we obtain the sorted list.",1.0
-2949,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort we select a pivot element which can be the first , last , middle or any other random element and then we partition the array with the pivot element as the divider.\nWe use divide and conquer algorithm as the basis for doing this sort  then individually we sort the small parts and lastly join them together to get our sorted array.\n",2.5
-2950,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort we select a random element from the array which could lie anywhere in the array and then we divide the array with the pivot element at wherever that random element lies. It could be the first , last or the middle element.",1.0
-2951,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-2952,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Insertion sort and selection sort are sorting algorithms with time complexity of O(n^2). The main difference between these sorting techniques is that in insertion sort, we sort the elements by comparing every element with its neighbors and placing it accordingly in the array. However in selection sort, this is not the way elements are sorted. ",0.0
-2953,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort take a pivot value and divide the array in such a way the left side al element should be smaller then pivot element and at left of the pivot all element should be greater then  pivot it does this operation in partition function. \nbasically it follows divide and conquer strategy to sort array \ntime complexity : O(nlogn) ,2.5
-2954,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,We take a pivot in the array and we have a partition function. An iterator( i ) moves from the left till it finds and element larger than the pivot and an iterator( j ) moves from right till it finds an element smaller than the pivot. If i> j then we swap the elements if not then the elements remain the same. The pivot is then shifted to the next element in the array and repeating these steps the array gets sort. This is how quick sort works.,2.5
-2955,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"A pivot element is chosen and then two pointers(i and j) are initialized, one which begins from the left of the array and the other which begins from the right. The pointers are traversing trough the array and the elements in the respective position are compared to the PIVOT element. Any element smaller than the pivot is inserted before the pivot. \n",2.5
-2956,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"We take a pivot element and two pointers are taken as reference ( I and j ) which are initialized at the beginning and the end of the array respectively, and move in forward and backward direction ( left to right and right to left). The pointers traverse the whole array comparing with the pivot and place the element correctly by satisfying the given conditions using swap function. \n when array[I] crosses array[j] swap the pivot and position of j pointer. Also when comparing the array if array[I] > array[j] swap the elements and move further.",2.5
-2957,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-2958,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, an element is taken as pivot and is inserted in its correct position by shifting the smaller elements to its right and the larger ones to its left. In this way the entire array is sorted.",2.0
-2959,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort has the worst time complexity of n log n which is better than the worst time complexity of merge sort but the best time complexity and average time complexity of merge sort and quick sort are same.,1.0
-2960,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,The basic steps involved in quick sort is:\n1)partition : we divide the given array in two parts \n2) pivot element: we take pivot element and sort the two different parts\n3) after sorting we merge the two different parts which is already sorted in a new sorted sequence\n  O(n log n ),2.0
-2961,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort works on the divide and conquer approach where the array is divided in parts using a pivot element which can be in middle ,start or end of the array and then merge in sorted order.",2.0
-2962,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,time complexity of quick sort is - n log(n)\nquick sort is a sorting technique helps to sort out the number in a required order .It makes 2 pointers temp and I for 1st and 2nd element and then check element one by one to know which one is greater than the other if a[i]>a[temp} then swap the function.\n\n,2.0
-2963,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick Sort works using partition method . \nIt uses Divide and Conquer approach.\nIt chooses a pivot element. Then it make comparison with pivot element and further it proceeds with quick sort Algorithm procedure. \nIt takes O(n^2) time. ,2.0
-2964,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort algorithm uses divide and conquer algorithm to sort any given array.,1.0
-2965,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,a quick sort works by selecting a pivot element and then arranging the elements according to the pivot element and then again repeating the steps till the array is sorted.,2.0
-2966,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,First we will apply the base case .\nThen we will two temporary pointers first one is for the element from which we will have to start the traversing of the array and next one is for the next element of the previous one.\nlets take them i and j\nin this we have cases when i is smaller than one then just return noting and when they are equal then also return nothing but when i is greater than j then we have to swap it depends upon from which side we are traversing and and in which order we will have to traverse.,2.0
-2967,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Linear Searching is the process of searching in which we search the elements by traversing all the elements in the given order one by one till the element to be searched is found.,0.0
-2968,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort uses a pivot element to sort the rest of the elements around it. it moves the pivot element to the right place and lesser elements to the left of it and greater to the right and then applies quick sort again in the left and right sepetrately. Works on divide and conquer algorithm.,2.5
-2969,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, elements are picked one by one and then shifting it to its correct position by counting elements smaller than it in the given array and then placing it.",1.0
-2970,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works by choosing a pivot element randomly and comparing all elements to it  and swapping when it encounters a larger element . it then stores it as partition variable and divides the array into two parts with indexes from 0 to p and p+1 to n and performs the above mentioned step till it reaches division of array length of  0. ,2.0
-2971,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,it is sorting technique that involves sorting the array be analyzing the values and then sorting according to them.,1.0
-2972,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In Insertion sort, the next smallest element is inserted in the array at it's appropriate place.\nIn Selection Sort, the smallest element in the array is found and selected to place it appropriately.\n",0.0
-2973,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"A quick sort separates the array into two parts and then compare the pivot element with each element if the sub array, if it is again not found , then it again divides the sub array into half ..it does it until the array is sorted in the order. The pivot element is the smallest  element of the array.",1.0
-2974,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works by maintaining a pivot element. We start two loops maintaining two pointers i and j, i starts from 0th index while j starts from end and traverse towards the start. If the ith element is less than the pivot element the pivot and element are swapped and if pivot is greater than jth element and pivot and jth element are swapped, when i crosses j the pivot is shifted to that point and the loop starts again and goes on till all the elements are sorted.",2.0
-2975,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quicksort is a sorting technique that have a complexity of (nlogn ) and is widely used in carrying out sorting.\nit calculates a pivot element and and then traverse the array which has to be sorted and check whether each element in array is smaller than or greater than pivot element and \narrange them according untill very end.\nPivot Element can be calculated as a first element or as a last element or as middle element.,2.5
-2976,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In the sorting technique the pivot element is compared with the other elements and then the positions of the element is changed accordingly.\nThis sorting technique uses a time complexity of O(n log n).,1.0
-2977,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In the sorting technique the pivot is compared to the other elements and changed accordingly.\nTime Complexity= O(n log n).,1.0
-2978,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, we select a pilot element and then compare it with other elements swap accordingly.",1.0
-2979,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort is a type of sorting technique. It works on the algorithm of divide and conquer. It takes a pivot element and two other pointers up and down. It then compares the pivot elements with up and down pointers element respectively and swaps the elements based n few conditions. \nIf pivot<=down move to next \nElse swap (pivot,arr[down])\nIf pivot >up then move to next\nElse swap (pivot,arr[up])\nIf up or down crosses each other ",2.5
-2980,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In quick sort  algorithm we take an element from array which is called pivot and place it on the position such that all elements on the left hand side of pivot element are smaller that pivot element and on right hand side all elements are greater than pivot element. This is done recursively to sort array.,2.5
-2981,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,QuickSort is a divide-and-conquer algorithm that selects a pivot element and partitions the array such that all elements smaller than the pivot are moved to its left and all larger elements to its right. This process is repeated recursively on each partition until the entire array is sorted.,2.5
-2982,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort works through the method of divide and conquer, it involves an array being split and selection of a pivot point and swapping of the elements based on the pivot point.",2.0
-2983,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, a pivot element is selected which can be fixed in front , back or middle of the list and then the list is divided in parts based on this pivot element and certain conditions . we sort the individual parts and then combine them finally ",1.0
-2984,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,It selects one element and puts it in it's rightful position and then do it for the elements on it's right and on it's left recursively.,1.0
-2985,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort uses a pivot point for the sorting purpose and categorize the equal to, less than, and greater than where the pivot point is again selected and the procedure goes again till the list gets sorted",2.0
-2986,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort takes a element as a partition and divides the array and then with the help of swapping, it arranges all the numbers which are smaller than the partition element to the left of the array and the elements which are larger than the partitioned element are arranged to the right of the array. The process repeats till the whole array is sorted. \nAlgorithm used - Divide and Conquer",2.5
-2987,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, \n1)choose a pivot element randomly from a list.\n2) All the elements lesser than the pivot are put to the left side of the pivot, and all the elements greater than the pivot are put to the right side of pivot\n3) The process is repeated for the left and the right side",2.5
-2988,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort uses a key value and then compare with other elements present in the array.,1.0
-2989,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"picks an element as pivot and partitions using it and then sorts , it is based on divide and conquer",2.0
-2990,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,It works on the model of Divide and conquer algo in which it is divided in two parts then sorted and recursive steps are call to receive a sorted data,2.0
-2991,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"its sorting algorithm its take O(n) time in sorting \nIts divide the number of element in 2 part and take the key and find whether the mid element is greater, equal and lesser than or not .if not again its divide its. ",2.0
-2992,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort we take a random element which works as a pivot and then partition the elements according to the pivot and placing the pivot in its correct position,2.0
-2993,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"We need a pivot point , then we consider all the elements smaller than pivot to the left of it and sort them , then we consider all the elements larger than the pivot  at the right of pivot and sort them . At the end we have a sorted array.",2.5
-2994,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort takes a middle element and sorts according to whether the coming element is greater or less than the given element ,2.0
-2995,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In quick sort we choose a pivot it can be last or first element of an array than we break it into two parts such that we have pivot+1 elements in one array and remaining in another array then we sort the array by keep on comparing it with pivot and meanwhile increasing the index of pivot.,2.0
-2996,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In the quick sort we define the pivot point where pivot point can be define as a first element of the array ,last element of the array and the middle part of the array .By the help of pivot point we can traverse the the i and j pointer so that sort the given array .and after the print the array. And the complexity is different for all the different pivot points.",2.0
-2997,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,it a divide and conquer based searching technique,1.0
-2998,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, we first define a pivot element, for which we can take the first, last or the middle element of the array. We then sort the array according to the pivot element such that it reaches its sorted position. We then repeat the process on both sides of the pivot element using recursive call until all the elements are sorted. ",2.0
-2999,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, a pivot point is selected and all the other elements are compared to that element. The elements smaller than that element are placed on the left side of that element and the elements larger than the element are placed on the right side. The same procedure is repeated on the left side elements and the right side elements.",2.5
-3000,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quicks sort works on the principle of divide and conquer,1.0
-3001,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works by taking a random element 'a' from the array and assigning the rest of the elements to three arrays s1 , s2 and s3. \ns1 contains the elements that are less than  a\ns2 contains element 'a'\ns3 contains elements greater than 'a'\nif |s1|>a\ncall the function with (a,|s1|-a,s2,s3)\nelse if |s1|+|s2| > a (a,|s1|+|s2|-a,s2,s3)\n",1.0
-3002,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort is one of the most efficient sorting algorithms. It works by breaking an array (partition) into smaller ones and swapping (exchanging) the smaller ones, depending on a comparison with the 'pivot' element picked.",2.0
-3003,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,It divides the array into parts till it got divided into individual units and then compares the 2 units at a time and then combined them in sorted fashion till the array gets sorted.,2.0
-3004,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is to find a pivot elemnt then comparing the elements if greater put right side else left side.,2.0
-3005,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort takes a pivot element and based on the value of the pivot all smaller elements goes left while all larger goes right of the pivot. The array is broken down into sub arrays like in merge sort and then we sort the basic sub arrays just to merge them and form the sorted array once again. ,2.5
-3006,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,\nin quick sort the array is divided in half with each pass till we reach upto the minimal level that is element level and then compare it with the array next to it to sort it in the correct order ,2.0
-3007,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quicksort is a type of divide and conquer algorithm for sorting an array, based on a partitioning it\nTo sort an array using quick sort we do following steps:\n1. You will make any index value in the array as a pivot.\n2. Then you will partition the array according to the pivot.\n3. Then you will recursively quicksort the left partition\nAfter this we will recursively sort the array.",2.0
-3008,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3009,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort algorithm is the process of arranging the element in the  some order as accending order or decending order this algo take the privote element and sort the element in better timr complexcity and give as trhe result in faster way.,1.0
-3010,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"We make a pivot element,\nWe start a loop, i starting from position 0 and j starting from position n - 1, comparing the elements, if the element is smaller than the pivot element, send it to the left side of the pivot element and if it is greater than the pivot element, send it to the right of the pivot element",2.5
-3011,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,we take a pivot point and then count the number of elements smaller than the pivot.Move the pivot to its original place.move the elements smaller from pivot to the left (order/sorting)doesnt matter and bigger than pivot to right pass both the array to quic sort again.this eventually selects pivots and keeps them in right places until the array is sorted.,2.5
-3012,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick Sort works by breaking an array into smaller ones and swapping the smaller ones depending on the comparison based on the pivot element picked.,2.0
-3013,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort we take a pivot element and compare the numbers from the pivot element such that small numbers are present on the left side and greater numbers are present on the right side,2.0
-3014,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works by taking a pivot either as start element,mid element,or last element,Then initialising i and j index Doing i++till we get an element at i that is greater than pivot and j++ till we get an element less than pivot, comapring with pivot and swapping pivot and A[j].It is a divide and conquer algorithm.",2.5
-3015,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works by taking a pivot element than comparing it with an element in the array and changing swapping elements if the element greter than or lesser than it is found depending upon pivot element that is chosen.,2.0
-3016,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort firstly select a pivot point and then place that selected pivot point at its correct location. ,1.0
-3017,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3018,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by breaking an array into smaller ones and swapping the smaller ones depending on the element picked.,1.0
-3019,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort works by assigning random (preferably first or last) element to pivot, and then using quicksort algorithm we rearrange elements less than pivot to the left of pivot and elements greater than pivot to the right of pivot. This algorithm works recursively until array is sorted.",2.5
-3020,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works on the Divide and Conquer principle.\nIt is partition's the array using Lomuto partition  and then joins it in a sorted manner.\nIt is a tail recursive function.,2.0
-3021,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort are the sorting algorithm in which we compare our element to the just next element of our array . If the next element of our array is less than the previous one then we performs swap function between those two element else we do noting.,1.0
-3022,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,first we chose a pivot and divide the araray in more 2 parts and we continue to do it and at the end we combine them while sorting happens.,1.0
-3023,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works first we have to find partition element it is generally the last element of the array then we find partition array in which we fix the correct position of the partition element and then with divide and conquer algorithm we make recursive call to find position for elements before partition element and after the partition element.,2.0
-3024,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is a divide and conquer algorithm. It has an average time complexity of nlogn but in worst case the time complexity is O(n^2)\n1. In quick sort an element is made a pivot element.\n2. The array is partitioned on the basis of the pivot element.\n3. Quick sort is recursively called on the partitioned arrays too till they become sorted.\n4. The sorted arrays are then combined and we get whole array sorted.,2.5
-3025,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort we choose a pivot element  and the ones which are less are arranged on the left side and greater ones on the right side and then again sorting is done in three sided after that the entire output is merged,2.5
-3026,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works according to divide and conquer approach, in this sorting method we make the use of \",1.0
-3027,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In insertion sort we place element one by one on its correct position . In selection sort we first find the minimum and maximum element and then compare other element with these minimum and maximum element and swap the element,1.0
-3028,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is done by taking a random element (for example the middle element and taking the element as the pivot and then sorting the array in the sense that all elements lesser than the pivot are sorted to the left side of the pivot and all elements grater than the pivot are in the right side of the pivot (Although not necessary in the sorted increasing order at first). Then we recursively call the quick sort algorithm for both the left and the right sides of the array. After this the array is sorted in an increasing order.,2.5
-3029,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,we try to place every element in its correct position in the array\nwe make two pointers and start traversing from first and then check if the last pointer value is lesser or greater\nif greater than move the pointer and else swap it and move the pointer forward and similarly repeat this\nyou will every element in its correct position. ,1.0
-3030,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Divide AND Conquer Algorithm\nwhich divides our let say array in two different halves one by one then recursively sort and combine the array\n,1.0
-3031,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,takes the first index and checks it whether it has any values smaller than that and post increment counter variable if it found the number . \nwe swap the first index with the index of counter value . \nthen we recursively checks both the sides of the first index value which is now positioned let say at x.\nthat's the working of quick sort in a nutshell.,2.0
-3032,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is the sorting algorithm based on the divide and conquer algorithm that picks an element as a pivot and partitions the given array around the pivoted point by placing the pivot in correct position in sorted array.,1.0
-3033,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,position indexing,2.0
-3034,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.," It works by breaking an array (partition) into smaller ones and swapping  the smaller ones, depend upon the  comparison with the pivot element that we have .\n",2.0
-3035,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,A pivot is taken in this sorting. That pivot element undergoes for further comparison with key. if its less than key then key is searched in array before the pivot. ,1.0
-3036,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort uses a partition to divide the array into two parts and then sort the two parts individually(placing all elements at their right positions). It uses divide and conquer approach.\nIt works on the complexity of O(logn).,2.0
-3037,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,
-3038,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, a pivot is selected (first, last or random) using partition algo and a recursive call is made until the elements before pivot are smaller and elements after pivot are greater and the array is sorted.",1.0
-3039,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, we define a pivot. then we bring all the values less than the pivot to it's left and all the values greater than the pivot to its right. After that we sort the left part and the right part recursively and we have  the sorted array.",2.0
-3040,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works on partition algorithm .we select a pivot element that can be either beginning or last or middle or random element such that the elements before it are smaller than it and elements after it are larger than it. then we recursively call for pivot elements until the array is sorted.,2.0
-3041,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort uses a pivot value, to split down the array. The values are compared at both sides, and then rearranged accordingly. The pivot is also switched with time and the flow of order of swapping. ",2.0
-3042,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In selection sort, we firstly calculate the largest/smallest element in the array and swap it with the first element. In second phase, we repeat the thing, but here we calculate the largest/smallest in the remaining array (excluding first index). We keep repeating the process until the array is sorted.\n\nIn Insertion sort,we divide the array in two parts- sorted and unsorted. We place the elements one by one from the unsorted part to the sorted part in their correct position",2.0
-3043,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Pivot is selected from the array. Then the array is divided into three parts cotaining elements less than, equal to, greater than the pivot. ",1.0
-3044,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, we take an element as pivot and we check for each element by taking two pointers and swap if it satisfies the IF CONDITION then if I>J then  we just swap the pivot with j.",2.0
-3045,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,It works by selecting a pivot element and breaking the array into smaller parts ,2.0
-3046,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"The insertion sort sorts a set of values by inserting the values into a presorted file. The selection sort, on the other hand, determines the least number from the list and arranges it in some order. Sorting is a fundamental procedure that involves placing an array's elements in a certain order to improve searchability.",2.0
-3047,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by\nStep 1 : finding a pivot point at a random index of the array\nStep 2: Arranging the array in such a way that the left side of the pivot is smaller than the pivot value and the right side is greater than or equal to the pivot value.\nStep 3:  Make a recursive call of the fn quick_sort on the both left and right parts of the array.\nStep 4: Continue this until the base case i.e. Size of array is 0 or 1 is met ,1.0
-3048,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"To sort an array, you will follow the steps below:\n1) You will make any index value in the array as a pivot.\n2) Then you will partition the array according to the pivot.\n3) Then you will recursively quicksort the left partition\n4) After that, you will recursively quicksort the correct partition.",2.0
-3049,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on the principle of divide and conquer technique. First we take a pivot element - it can be first element, middle or last. Then we fix that pivot element in it's correct position by using basic for loops. Then we divide the array in parts - from 0 index to pivot-1 and pivot+1. We will not take that pivot element in role as it is already sorted. Then we will apply recursion on both arrays, and the process goes on and we get a sorted array. Time Complexity - O(nlogn).",1.0
-3050,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works on the divide and conquer approach. it gives the ight position of a pivot and the elements on the left of the pivot should be  smaller than pivot and right elements should be greater and then it works the same way on both the sides.,2.0
-3051,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,it works on divide and conquer. array is divided on pivot element then the each part is sorted separtly .,1.0
-3052,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick Sort works on the basis of position indexing.\nThe position array is created which is then converted into sorted array using the indexes.,2.0
-3053,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,),0.0
-3054,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3055,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"It works by partitioning an array into smaller arrays and exchanging the smaller ones, depending on a comparison with the 'pivot' element picked up.",1.0
-3056,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort a pivot element is chosen at random and then the whole array is sorted with respect to that element after that array is divided from the pivot in two parts and those two parts are sorted with a new randomly selected pivot for each part and the process continues till size of divided array minimizes to one and array gets sorted.,2.0
-3057,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort work on divide and conquer approach . First array is divide into two parts then further divided into two parts until it reach to one element then compare b/w to element then combine the array.,1.0
-3058,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is a divide and conquer sorting algo in which a pivot element is chosen which then divides the array in two parts. Left part contains elements smaller than pivot element and right part contains elements larger than the pivot.,1.0
-3059,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In Insertion Sort the priority element(Greatest of smallest in the remaining array) is selected and putted into its correct position by shifting the other elements in the array.\n\nIn selection sort, the priority element is swapped with the element in its correct position.\n\nTime Complexity - O(n^2)",2.0
-3060,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort use use min heap concept for reducing the time complexity of the algorithm  , it use min heap concept and reduce it to (nlogn) time . ",1.0
-3061,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,it uses increments and as the loop progresses it arranges the order of elements till that particular range. It uses comparison\nton sort out the order among elements and is great for larger data sets.,2.0
-3062,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works divide and conquer method.,1.0
-3063,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort it divide the array into some parts and then perform sorting and arrange in ascending order as the need of the problem,2.0
-3064,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort work by taking a pivot element and then partitioning the array into three parts one which contains elements smaller than pivot, other one which contains elements equal to the pivot and the last one containing the elements greater than the pivot. Then after this the process repeats itself recursively on the 1st and 3rd parts, hence giving us a sorted array.",1.0
-3065,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort is a searching algorithm in which we choose a pivot value from the array and make the two array and then sort the element in both the with the help of pivot element.,2.0
-3066,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In quick sort the one element is taken as a pivot and its correct position is taken out and then recursively we find the pivot for all the remaining array,1.0
-3067,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, first we partition the array and sort both the arrays left and right of the partition. Then we sort both these arrays using quicksort recursively until we have one element which is already sorted.  ",1.0
-3068,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works on the basis of partitioning using a pivot element where we can partition the array by taking either first element or middle element or last element of the array as pivot and the array keeps on getting divided by pivot elements till the time one element is left in each partition and then sorting of elements occurs in the array,2.0
-3069,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in insertion sort the minimum element is first assigned its correct position and then the further next minimum element is taken into consideration by this method the entire array is sorted.\nin selection sort the user selects a particular element which is the assigned its correct position and then with respect to it other elements are sorted.\nhowever both have same time complexity of n square. ,1.0
-3070,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works by creating partitions ,1.0
-3071,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In quick sort we select an element and traverse the array and arrange elements such that all the elements smaller than our selected element are on the left side and the larger ones on right side . then we again recursively call quick sort on the left group of smaller elements and right group of larger elements . ,1.0
-3072,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by selecting a pivot in the array and then sorting all the elements smaller than the pivot and thus placing the pivot at the right place. It uses recursion and keeps repeating this process till the complete array is sorted. \n,2.0
-3073,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Most efficient algorithm. It Works by partitioning an array into smaller ones and swapping the smaller ones depending on comparison with the pivot element.,1.0
-3074,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort, we select any one element as pivot (either from start , middle or end ) and we sort the data structure as per the pivot like on the left side the elements smaller than pivot are present and right side the elements greater than pivot are present or vice versa.",2.0
-3075,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in this first we take an pivot element and place it at the correct index where it should be in the sorted array. we do this using recursion and divide and conqour approach.,1.0
-3076,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"These are different in approaches used to solve them. Insertion sort work as if we insert each element again and hence sorting them. Selection sort, as the name suggest work as if we have selected an element and then sorting it by comparing with every other element. ",2.0
-3077,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort involves a pivot element and based on the pivot it divides the array into three parts one the elements to its left that are the ones smaller than itself, second the elements equal to itself and third the elements larger than itself onto its right and it goes on doing this recursively on the two parts the left and right one to sort the entire array.",2.0
-3078,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on the basis of partitioning. First we select a pivot element and then partition the elements according to the pivot element and then sorts the array in the required order. The pivot element can be first element, last element, or middle element. ",2.0
-3079,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In Quicksort, we simply take one element whether it may be first or last or any element and declare it as pivot. Then we partition our array on the basis of pivot in such a way that the pivot element reach at its correct position in the array. We repeat the process until the array is sorted.\n",2.0
-3080,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works on the principle of divide and conquer. First we take a pivot around which we divide the array the first half has the smaller than elements and the second half has the larger one. In this way we keep on dividing the array and lastly combine them.,2.0
-3081,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort works using the divide and conquer approach. quick sort uses a partition element which can be any elements from the array. and sort the array according to that partition the elements lesser than the partition element move to the left of it and the bigger ones move towards the right, the movement is done by swapping the elements. The process is repeated again and again taking different partitions until the whole array is sorted.",2.0
-3082,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on divide and conquer method and the partition method for sorting an array. In the partion we use a pivot either from begin , end , or accoding to desired location. \nThe complexity of this algorithm depends upon the selection of the best pivot,\nThe average case time complexity of the quick sort is O(nlogn) while in worst cases it can go to the O(n^2).",2.0
-3083,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort we take a pivot element form the array and divide the array in two parts according to pivot element and decide new pivot element for new arrays and repeat the process\nthen sort the array and merge them one by one;,2.0
-3084,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works in following manner.\nIt divides the array by selecting a pivot element for the elements in the array which are greater than pivot are inserted into a new list and for smaller than pivot into another list.\nthen these lists are sorted and merged to obtain the original array as sorted ,2.0
-3085,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"it first find a partition and then arrange the elements accordingly , it works recursively by repeating the same and giving the required result at the end.",2.0
-3086,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick Sort works works on the divide and conquer rule. A given array is divided and then sorted part by part known as partitions, to form a sorted array. ",2.0
-3087,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3088,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on the divide and conquer method, where you create partition in the array on the basis of comparing and the merge it in the ascending order.",2.0
-3089,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort is sorting technique which sort the array by comparing the least element with every element.,2.0
-3090,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on the principle of divide and conquer and has Time Complexity of O(nlogn). it works by having three pointers. one will point at pivot. the other at beginning and the other at end of array. Pivot can be beginning, end or middle (any) element. i moves from right to left. j moves left to right. when the cross the elements are swapped and elements reach their correct place. Multiple iterations of the same result in sorting of the array. In each iteration the size of the array we are working with gets reduced as we dont need to look at the already sorted part. ",2.0
-3091,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sorts follows divide and conquer algorithm. We take a pivot element  and the elements smaller than the pivot element are brought to the left of it and the greater are brought to the right of it. and as soon as the pointer for bigger element surpasses the smaller one , the element pointed by the small element pointer becomes the pivot. This is done till the array is sorted.",2.0
-3092,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort uses divide and conquer approach it picks a random element and then divide the array around it.,2.0
-3093,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In quick sort we use two pointers one for left and other for right left pointer traverse from left to right and right traverse form right to left.\nWe select one pivot element and if [key+left]/2<mid then pointer then left pointer move towards right and if [key+left]/2>mid then second pointer moves from right to left.,2.0
-3094,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,QuickSort is a Divide and Conquer algorithm. It picks an element as a pivot and partitions the given array around the picked pivot. There are many different versions of quickSort that pick pivot in different ways,2.0
-3095,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.," It works by dividing an array  into smaller arrays and changing he smaller arrays , depending  on the pivot that is taking element from both the  arrays are either incremented or swapped accordingly with smaller element present in the first array.",2.0
-3096,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3097,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works work divider and conquer algorithm\nit is a sorting technique to sort the data accordingly,2.0
-3098,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In quick sort we use to take a pivot to sort the whole array. we use to take  a pivot and compare it with the next element if the elment is small than we use to change the position of the element with the next one.,2.0
-3099,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort breaks an array into smaller arrays and then swaps the smaller ones, by comparing with the adjacent element.",2.0
-3100,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3101,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works with the help of a divide and conquer algorithm. Here we take the help of a recursion.\nHere first we find a partition element then we place it at its correct position then we sort the elements present before the partition elements and the elements after that partition element with the help of recursion and divide and conquer algorithm.,2.0
-3102,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,you select a element as the pivot and put all the elements in their correct places with respect to the pivot that and do it recursively until the array becomes sortes,1.0
-3103,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,We choose a pivot element and place all the smaller elements to the left of it and all the larger elements to the right of it. Then using divide and conquer we break the array from that element in two parts and solve both arrays using recursion using same method. ,2.0
-3104,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by taking one element at a time(pivot) and then placing it in its correct postition then calling the function recursively for this elements left and right side,1.0
-3105,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works by the method of divide and conquer . An index called pivot is first taken then the array is divided into 3 parts . 1st where the elements are smaller than the one at pivot ,2nd where the elements are equal to the one at pivot and 3rd where the elements are greater . now at each step the quick sort is called recursively for each of the part . And then finally the we get sorted array at the last and the recursion is called back.",2.0
-3106,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,divide and conquer,1.0
-3107,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,so in a quick sort first of all we take up any element as pivot element .\nthen we form a partition where all elements smaller than pivot element is put to the left of it and all elements greater are put to the right of it.\nthen we start comparing and placing every element to its correct position .\nit works in O(nLOGn) time.,1.0
-3108,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is a sorting technique in which we first select a random element in the array which we call as pivot element and then we put all elements which are smaller than pivot in its left and bigger in right this places our pivot element at its correct position. Then we se;ect another pivot in left and right side so to sort the whole array. Its average time complexity is O(nlogn) and worst case is O(n^2).,1.0
-3109,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,The function of quick sort is to sort the given array in O(n*logn) time complexity. \nIt uses the divide and conquer approach to solve the problem. An element is taken from the given array and then compared with all the elements by finding the largest element in each traversal and then placing that largest element in the last of the array. The loop runs till the count of the elements. And in such manner quick sort works.,2.0
-3110,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quicksort follows divide and conquer algorithm. in which we select a pivot element and arrange the element lesser than it in left and greater than it in right to sort thr element.,2.0
-3111,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.," The algorithm picks a pivot element and rearranges the array elements so that all elements smaller than the picked pivot element move to the left side of the pivot, and all greater elements move to the right side. Finally, the algorithm recursively sorts the subarrays on the left and right of the pivot element.",1.0
-3112,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort we pick an element in random and check the number of elements smaller and larger than it \nthan we put the chosen element in its position in the array (if it were sorted)\nthan all the elements that are larger than it are put to its left side and the larger elements on the right side\nand we call the function for the left part of the array and the right part of the array,1.0
-3113,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on partition theorem (Lomuto,Hoare partition theorems)\nin quick sort array is partioned about an any element (mainly first and last) and then recursive call done for two arrays while is the part of main array that is divided due to the pivot element and we repeat process untill 1 element left after that we get the sorted array",1.0
-3114,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,element is put in on its position and on left small elememts are kept and on right greater elements than the elements are put and for other elements recursion is called,1.0
-3115,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Linear searching is a simple method of searching whether an element exists in any particular data structure, It takes the required element and compares it with each of the element present in the data structure. If found. it gives us the required result .",1.0
-3116,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,It works by breaking an array into smaller ones and swapping the smaller ones depending upon comparison with pivot element,1.0
-3117,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"We get a random data from amongst the array of data available. Then we compare the value of this data with the other datas. Then we divide the array into two factions, then repeat the above procedure until we are left with NULL. Then we combine the elements in  the order that they were divided and we have the sorted array.",1.0
-3118,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In quick sort technique we first choose an element from our array as a pivot element. Then placed the elements such that the elements which are smaller than the pivot element should placed on the left of it and elements which are greater should be on the right of it.,1.0
-3119,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort is sorting technique which basically works on divide and conquer approach it find pivot element and arrange element on left and right according to the pivot.Then pivot element for two partitioned arrays are generated till the array is sorted.  ,2.0
-3120,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick sort works on the principle of divide and conquer.It first divides the problem into smaller smaller problems,solves those and then combines those smaller problems that is it divides the array to be sorted in smaller arrays and then combines those smaller sorted arrays to get the sorted array as the solution.",2.0
-3121,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In this we take a pivot element randomly and we place all the elements in either left or right of the pivot and hence we find the sorted array. ,2.0
-3122,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3123,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort is based on divide and conquer technique its working is as follows:-\n1-in quick sort we randomly select a pivot element from the data\n2-we find all the elements less than and greater than pivot\n3-then we place the pivot at its correct position\n4-then we call recursion,2.0
-3124,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,a pivot element is chosen randomly from the set of elements and all the other elements are placed either to the right or left to this pivot element,2.0
-3125,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort., <<endl;\n  }\n}\n\n,0.0
-3126,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort works on the principle of divide and conquer . in quick sort we find the pivot element and it is placed at its correct position when we call it recursively,1.0
-3127,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by finding an element known as pivot whose correct position in the array/vectors is obtained  and is placed according to it's correct position. Now the elements less than this pivot element are placed before it and  greater/equal elements are placed after it. This repeats for the smallest size array to the biggest and thus sorting is performed in O(n log n ).,1.0
-3128,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"follows divide and conquer approach, in which we break down the array into single element then perform some operation on these elements, and at the end we have our data sorted.",2.5
-3129,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3130,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort work by finding the position of piviot element and sorting accordingly and doing this in reccursive manner.,2.5
-3131,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In the array first we take the middle term as pivot or the last term as pivot .then here two array will be created that the parting of the array and compare the arrays rem one by one with the pivot and give in final array and that array will be sorted in the partition of an array is the approach  ,2.5
-3132,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"in each iteration, an element is picked and it is swapped till it reaches its correct position in the array, that is, greater elements are on the right and lesser elements go to the left.",2.0
-3133,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort we run a iterator and compares the every element starting from the index 0, whenever the value of right index surpasses the value of its imediate left index we swap them. We perform this process again and again until we get a sorted array.",1.0
-3134,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort finds the correct position of a pivot element from unsorted array in the sorted array and place it at that position and in this way it finds the correct position of each element and hence, sorts the array",2.5
-3135,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by selecting an element as pivot and sorting array around the value of the pivot such that the value greater than the value of pivot come after index of pivot . Then updating the pivot and again following the same procedure till the array gets sorted.,2.5
-3136,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"there are different of sorting like Merge sort,quick sort etc.\nsorting is basically the way to sort given array in different ways like in merge sort we divide the array in two equal parts till it reaches upto single values \nsimilarly quick sort works in a way that we have to compare first element to every other element if it is smaller then we have to add it in the last and check for another element and compare it to every other elements and so on",2.0
-3137,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort divide the array in two parts than sort both parts in a order,2.5
-3138,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3139,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort works by first partioning the array about a pivot element(random element from the array) so that the pivot element comes to its correct position where it should be in a sorted array. And a recursive function should be runned from low index to pivot index and pivot+1th index to high index in a recursivfe manner so thaat every time the recusion is done pivot comes to its right position and at the end we need to swap the pivot with high element in order to get a fully sorted array.,2.5
-3140,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in this we select a pivot element and then acc to this pivot element we divde the array and sort it in 2 parts and then doing this recursively,2.5
-3141,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,it works on the concept of divide and conquer it first sets the pivot element and the one by divide elements into subarrays by using pivot and then compare elements on the most basic level,2.5
-3142,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3143,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"Quick Sort function utilizes the use of partition function which actually selects a random element from the array and place it according to logographical order then elements smaller than it are placed on the left and vice versa with the larger elements, partition index is returned for which the quick sort is called two times one for start to pI and from pI to end.\n",2.5
-3144,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in this we select the priort element and acc to this priot element we divide the list in 2 parts ,2.5
-3145,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort first partitions the array about the pivot element. It then puts all the elements less than the pivot element before it and all elements greater than pivot element after it in the array.This runs recursively until entire array is sorted.,2.5
-3146,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort a pivot is selected \nthe i+1 th elements is compared with ith item\nif i+1<i then we swap\nwhere i =selected pivot,2.5
-3147,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"sorting is used to do simply things, quick sort quickly sort all the functions, entities in the program",0.0
-3148,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,selecting the middle element of an array as pivot \nkeeping the smallest to pivot the left and larger on the right \nsorting both arrays separately and at last place the pivot on it's position ,2.5
-3149,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort first partions the array in two different part and then it sorts the elements in such a way that the element to the left of pivot (partition point) is smaller and to the right is is biggger.,2.5
-3150,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3151,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort their is three things are given one is first  sub array and middle pivot element and second sub array. and we move in two loops to check the value that the element is lesser or greater than pivot element.,2.5
-3152,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort work in time of (log n) it works on the concept of divide and conquer method to achieve a particular result in the given array \nso firstly it divide the hole array into a single  element and the sort it on the bases of ascending order   and then give a desire result  ,2.5
-3153,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort randomly selects a pivot element and divides the structure according to it. The elements that are smaller than that goes to the left and the elements bigger goes to the right. It then keep dividing them further in the same manner till single elements remain and then starts merging them to form the final sorted result.,2.5
-3154,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort first we take a pivot element and then compare it element starting from right and left \nif the left one element is smaller then we replace it with greater and if right one element is smaller then we shift the pivot element  to it,2.5
-3155,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3156,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,a quick sort first sort the given array the find the pivot  from the given element and the compare it from all elements and by this process we find and sorted and  optimal array.,2.5
-3157,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"first a pivot element is to be chosen then, elemnts get indexed or traversed as per its order means element grater or less than the pivot element ",2.5
-3158,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,In Quick sort. \n1. We define a pivot element and start comparison from right to left.\n2. If left element is smaller than we replace it with the greater element \n3. If the right element is smaller than we shift the pivot element to it.,2.5
-3159,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick sort we compare the element from starting index 0 and compare the element such that we swap the two elements if they arent in order then we compare the next element in the array then we again compare the previous ad the first element and so on.,0.0
-3160,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"in quick sort two pointers are taken and one pivot point is taken we put conditions for elements if the right pointer satisfy the condition then it shift toward left, if pivot is being crossed then both values are swapped.",1.0
-3161,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort divides the given array in two parts in which the point from where we divided , the left side is occupied by the elements which are smaller than the pivot and the right side is occupied by the elements greater than the pivot (pivot - middle element of the array). after all the sorting process we get an array which is in ascending order.",2.5
-3162,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"In quick sort , we first select a pivot element. Then we use recursion to keep the elements lower than the pivot element to the left of the pivot and the element greater than the pivot element to the right of the element and do this until the array is sorted",2.5
-3163,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,Quick sort algorithm works on the array by traversing array from both the ends start to end or end to start and arrange it in increasing or decreasing sequence,1.0
-3164,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in this an element is made pivot and then we compare pivot to all the elements in an array  and when pivot comes in middle we divide the array into two in which left side of pivot is smaller than pivoted element and right side is greater than pivot and again follows same process.,2.5
-3165,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,,0.0
-3166,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"in quick sort, first the a pivot is declared in the middle of the array then two pointers one at the beginning and one at the last respectively. The pointer at the start of the array moves forward and checks weather the value of pivot is greater or less than it. If the value is more, they digits are swapped, if it is less then the pointer moves forward. similarly The pointer at the last of array moves towards the left comparing the digits and then swapping the values of last and first pointer if the digit is less than it. Now the left side of pivot contains the digits smaller than it and right side greater. Now this function is again called for the left and right side of the array respectively, as a result the array is sorted.",2.5
-3167,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,quick short short a date and bring  a desirable output to the audience ,0.0
-3168,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"it takes a pivot point and breaks the left array into sub arrays taking centre as pivot, and it continues till we reach to individual items and the we rejoi the values by compaing them.",2.0
-3169,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in quick sort we having i and j i move to end of array and j move to starting of  array I is find bigger element and j finding smaller element swap them again doing same thing ,1.0
-3170,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,pivot elements.,0.5
-3171,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,in this sorting algorithim the partition is create in an unsorted array in which the first part is sorted and corresponding the second part is sorted ,0.0
-3172,How quick sort works?,Quick sort uses divide and conquer approach. It divides the list in smaller 'partitions' using 'pivot'. The values which are smaller than the pivot are arranged in the left partition and greater values are arranged in the right partition. Each partition is recursively sorted using quick sort.,"quick sort working is similar as selection sort,",0.0
-3173,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",The process of travelling/visiting every node of a tree is referred to as tree traversal.,1.0
-3174,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","there are 3 tree traversals which are preorder, postorder, inorder . tree traversals are generally an idea to visit every node of a tree in division of three which are either root->left subtree->right subtree, left subtree->right subtree->root, left subtree->root->right subtree respectively",2.5
-3175,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",In tree traversal all the elements which are present in the tree are exactly visited once tree traversal can be done in many different ways like BFS which is breadth first search and dfs which is depth first search  ,2.0
-3176,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal refers to traversing the tree data structure starting from root node than moving to every child of the tree,2.0
-3177,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal refers to the accessing the elements inside the tree. It can be done in 3 ways, preorder traversal, inorder traversal, postorder traversal. \nPreorder traversal starts accepts the root node as input, traverses root first then the left child and at last the right child.\nInorder traversal traverses left child then root then right child.\nPostOrder traversal goes left child then right child then root.",2.5
-3178,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is used to traverse every node of tree data structure. \nTraversing every node from root to leaf ,2.0
-3179,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is a method in which left sub tree is visited first then root is visited and at last the  left sub tree. ,1.0
-3180,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is of 3 types postorder, inorder and preorder. Through tree traversal we write all the elements of the tree . Like in postorder traversal first it takes the left node then it takes the right node then it takes the root node.",2.5
-3181,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is visiting each and every node in a tree\nthere are three types of tree traversal \npostorder (first left then right then node)\ninorder (first left then node  then right)\npreorder (node left then left then right),2.5
-3182,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is a process to visit all the nodes of a tree to carry out a particular function and may print their values too.,1.0
-3183,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the traversing or visiting the each node of the tree .,1.0
-3184,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Checking or observing any node of a tree in order to obtain the solution of your search is called tree traversal.,1.0
-3185,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal  means  traversing  tree  in DFS or BFS form and going through each node and storing it's data and then retrieving it.,1.0
-3186,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","There are three types of traversing/ looking through/ reading the values stored in a tree, which include inorder preorder and postorder treversal. \nInorder: left child-> root -> right child\nPreorder:  root ->  left child-> right child\nPostorder: right child-> left child -> root",2.5
-3187,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",moving on each node of the tree is called tree traversal.\n ,1.0
-3188,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Exploring/visiting each and every node of the tree is know as tree traversal.,2.0
-3189,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree is a data structure and there are three tree traversal techniques that help us read data from all the nodes without leaving any node\n1. Breadth First Search : In this, start from root node and store all the children of root node in a queue and then take an element from queue, explore that node and keep repeating this till all nodes are found/read\n2. Depth First Search : in this  start from root node and store all the children of root node in a stack and then take an element from stack, explore that node and keep repeating this till all nodes are found/read\n3. Level order Traversal : start from the first level, explore all the nodes on that level and then go to the next level and keep on repeating till you hit the bottom level",2.0
-3190,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","In quick sort, there exist a pivot which plays a major role in sorting the array quickly.\nIt sorts the array in such a way that the value before pivot are smaller than it and the values ahead of the pivot are greater than it.\nIt is hence to be checked again and again until pivot reaches to the last.",0.0
-3191,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is the technique in which we traverse each node from root to leaf of the tree effectively ,it can be post, order ,pre order,  inorder  traversal",2.5
-3192,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is traversing a list of elements in a tree using different approaches.,1.5
-3193,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is a pattern for traversal in a tree which covers every node of a tree in a particular set pattern .\nExample- Preorder traversal => root->left->right (It utilizes this pattern to print all the nodes of a tree)\n                In order Traversal => left->root->right\n                Post order traversal => left->right->root,2.5
-3194,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is meant by visiting every node that comprises of a tree, and it could be in any order. \nexample:  preorder(root->left->right)\n  inorder (left->root->right)or\n postorder (left->right->root)",2.5
-3195,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is a method of exploring the every nodes of tree from its  root to its children node. The time taken to traverse the tree is O(logn)\nMethods of tree traversal are:\nlevel order traversal \npost order traversal \npre order traversal\ninorder traversal \n,2.5
-3196,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Quicksort is one of the most efficient sorting algorithms. This is because it has time complexity of O( nlogn ). In quicksort, a partition element is choosen, which divides array into two subarrays and the array is sorted accordingly. For example, if A={7,3,9,5,8,12} is an array with partition element 5, all elements less than 5 will come to the left of 5 and all elements greater than 5 will be placed to the right of 5.  Since this array is still not sorted, we will apply same algorithm on the two subarrays which are present on the left and right of 5. Hence by divide and conquer approach, eventually entire array will be sorted. ",0.0
-3197,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is where we go through each and every node and print there data  through our print function,\nexample :level order traversal ,preorder , Postorder , Inorder .",2.5
-3198,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",When we are searching for an element in a tree and if the element is smaller than the root we go left side of the parent node otherwise when it's larger we go right side of the parent node and this process continues as we keep on moving left and right until we find the element in the tree.,2.0
-3199,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",The traversal of a tree from its root note and visiting every single node until it reaches the root node is known as tree traversal.,2.0
-3200,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","The traversal of a tree initiating from the root node and visiting its each child using preorder, in order and post order traversal basis.",2.5
-3201,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",travelling from root to the each leaf node of the given tree is called tree traversal.,2.0
-3202,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tress traversal means visiting all the nodes of a tree one by one from root to the last leaf node.,2.0
-3203,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","travelling a tree is known as tree traversal. there are three types of tree traversal which are LNR, NLR, LRN                  left node right, node left right, left right node respectively\nin LNR first the left side of the tree is traversed then the node is printed then it goes to the right side. Similarly all the other traversals work.",2.0
-3204,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is of two types:\n1) we build a tree and travers the value by level order , level order means we traverse the each and every element at height =1 then we go to higher height\n2) we build a tree and traverse its value like going left side of tree or right side of tree, means we are going left until we find null value .  ",1.0
-3205,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is basically going to each node of the tree once .,1.0
-3206,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the process in which the top most node is the root node and all nodes are connected with 2 sub nodes(right and left ).right node (greater value than root) left node(lesser value than root) If u want to traverse to a particular node u start with root node and the on the basis of whether particular node is greater than root node or not u traverse to go to the right node or the left node. ,1.0
-3207,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","there are Three methods for tree traversal. \n1. In-order- first we traverse left part of the root  node , then root node , and then right node. \n2. Pre-order - First we go to the  root  node , then traverse the left part and then the right part. \n3.Post-order - first we traverse the left part, then right part and then to the root node . ",2.5
-3208,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal refers to the process of visiting nodes in a tree. Tree traversal can be divided into in-order, pre-order and post-order.",2.5
-3209,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is visiting all the nodes of the tree including root node, parent node and child node.\ntypes of tree traversal are:\nin-order ,post-order and pre-order.",2.5
-3210,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",The traversing of the the nodes and child nodes of the tree data structure by going top to bottom or bottom to top which the help of temporary pointer is called tree traversal.\nIt can be used in many ways like finding the element and inserting the element or deleting the element or comparing the 2 or more elements.,2.5
-3211,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","In insertion sort, we divide the given array in two parts such that 1st array has 1 element which is the first element of array and 2nd array has all other elements in same order. Now, we traverse the 2nd array and find smallest element and add this element to 1st array in sorted order. We repeat this step till 2nd array becomes empty and first array has all the elements.\nIn selection sort, we take a pivot element and start comparing elements from start and end simultaneously and swap when any condition satisfies without dividing the array.",2.5
-3212,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal refers to accessing each element(node) of the tree one by one in some order.eg: inorder, preorder amnd postorder.",2.0
-3213,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversal in a tree, to find an element is present in it or not is known as tree traversal.\nIt can be done in three ways:\nInorder (left, root, right)\nPreorder (root, left, right)\nPostorder (left, right, root)\n",2.5
-3214,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversing the tree from its root node to its both child all along till the leaf nodes to access each nodes in the tree is called tree traversal . It can be done by traversing the nodes in three ways :-\ninorder (left, root ,right)\npreorder(root, Left, Right)\npostorder(Left, Right, root",2.5
-3215,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in tree traversal what we do is that we perform traversal on the tree to look out for the desired solution. traversal is just like travelling from one of node of the tree to another just to find out the solution.\ninorder\npostorder\npreorder,2.5
-3216,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","In Quick Sort, a random element from the array is chosen as the pivot element (ideally, the median) and then that pivot is compared to all the elements and the elements are sorted according to it. The elements smaller to pivot on its left and the elements larger than pivot on it's right. Just like that, the array is sorted.",2.5
-3217,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","The traversal of elements of a tree array based on the root node, left node and right node.",2.5
-3218,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree is a data-structure and tree traversing means visiting each and every node of the tree to search for a particular element. We search for the elements starting from root node and going down to the child and leaf nodes of the tree.,2.5
-3219,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Accessing all the values stored in the node of tree starting from first node that is root node to all the leaf node until the pointer which is traversing gives a null is called Tree Traversals.,2.5
-3220,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the process of exploring each and every node of the tree till all the leaf nodes are covered. ,2.5
-3221,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",The process of accessing the different nodes of the tree is called tree traversal.,2.5
-3222,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",The process of traversing(or visiting) each element of a tree is known as tree traversal.,1.5
-3223,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree Traversal is the moving of pointer at every node of tree from root to the leaf in order to print the stored data or search the element stored in a tree. Inorder,Preorder and Post order are few types of tree traversals.",2.5
-3224,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is a technique to traverse all elements of a tree node.  types of tree traversal :\n1. Preorder traversal\n2. Inorder traversal\n3. Postorder traversal\n\n,2.5
-3225,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",To travel from source node/vertex to goal node in a tree ,2.5
-3226,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","it is traversal of a tree and has 3 types, left data right (ldr), left right data (lrd), data left right (dlr), where each of this reperresents the child to be traversed with repsect to the parent node. It can be easily implemented by using recursion. ",2.5
-3227,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is visiting the nodes of tree until the required outcome is obtained.,2.5
-3228,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal means in which manner we visit the nodes of the tree.,2.5
-3229,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",the tree traversal is printing a tree in simple language via going through each and every node present in the tree,2.5
-3230,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is traversing each node of the tree from root to the leaf nodes. ,2.5
-3231,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the process of accessing all the elements of a tree\nThere are several tree traversal methods. Some include\n1) Pre order traversal \n2) In order traversal\n3) Post order traversal\n4) Level order traversal,2.5
-3232,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Traversing or traviling to each node of a tree is called tree traversal.,2.5
-3233,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",traversing to all the branches of a tree ,2.0
-3234,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",It means visiting each node at least once ,1.0
-3235,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",traversing to all nodes by level- by level,1.0
-3236,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal meaning walking/traversing through a tree i.e. going through every node of the tree until a specific node is reached. There are three ways in which we can traverse a tree:\n1. pre-order traversal\n2. in-order traversal\n3. post-order traversal,2.5
-3237,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal could be defined as the process of searching something inside the tree, as we start from root go to its leftmost child , then search in its child, come back(if not found ) then again search in the next child of root node and keep searching until the end of tree.",1.0
-3238,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","traversing the given elements in a tree based on their sizes with the smaller element taking the left and bigger one sticking to right. Based on M or the number of elements that can be entered, the levels  gave m-1 values",1.0
-3239,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is a type of linear search where we can find the node or the string by traversing the tree .,1.5
-3240,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",In the tree traversal  we traverse to root node to the leaf node.\nTheir are thre types of the tree taversal :-\nINORDER TRAVERSAL \nPREORDER TRAVERSAL\nPOSTORDER TRAVEWRSAL ,2.5
-3241,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",it is traversing of a tree from its root node to it smallest leaf node .,1.0
-3242,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal refers to visiting all the nodes of a tree at least once. For each node, we define if it is unvisited, visited or completely explored. Using various algorithms, we can then traverse the tree until every node has been completely explored. ",2.0
-3243,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Visiting each node of the tree is called tree traversal. Tree traversal can be used to search any element in the tree.,2.5
-3244,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",it is terminology given to iterating a tree data structure.,2.0
-3245,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Traversing each node of the tree is called tree traversal.\nFollowing are the the types: \n1. In-order traversal : Left child node->parent node->right child node\n2. Pre-order traversal :  parent node->Left child node->right child node\n3. Post-order traversal:  Left child node->right child node->parent node\n4. Breath first traversal /  level order traversal : each node is traversed level wise,2.5
-3246,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree taversal is a technique in which the move through all the elements in the tree exactly once and print them in the desired techniques.\nthere are basically three traversal techniques, Preorder Traversal, Inorder traversal and Postorder traversal.",2.5
-3247,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal means to print the value stored in every node of a tree.,1.0
-3248,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is traversing each and every node of a tree following are the possible traversals:\n->Pre Order\n->In Order\n->Pos tOrder,2.0
-3249,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",When a data structure called tree's every node is visited in accordance to the traversal method then that is called tree traversal.,2.5
-3250,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the process of traversing every node of the tree and exploring the child node of every till all the nodes of the are covered \ntraversal types are preorder,postorder,inorder",2.5
-3251,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the process of visiting each node in tree exactly once. Traversing can be done by three ways inorder, preorder, postorder.",2.0
-3252,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",visiting all the nodes of a tree is known as tree traversal ,1.0
-3253,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is the traversal in tree on each node  once and then ,1.0
-3254,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree Traversal is a method to explore/visit different nodes in a tree,1.0
-3255,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","visiting every node in the tree is known as tree traversal.We have different types of traversal like LDR,RDL,DLR",2.0
-3256,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree Traversal is a method of visiting each node a node in a systematic order.,1.0
-3257,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is visiting each and every node of a tree,2.0
-3258,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is technique where traversing starts from root node to leaf node.Every node has a parent node.In BST THE RIGHT SIDE>PARENT AND LEFT SIDE<PARENT so we get min and max elements easily.,2.0
-3259,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",it involves visiting each node of a tree once.,1.0
-3260,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree Traversal is the technique by which we can search through the nodes of the tree . \nThere are many ways by which we can traverse along a tree - Breath First  Search (BFS), Depth First Search (DFS)",2.0
-3261,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the traversal of  a tree from start node to leaf node. ,1.0
-3262,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is visiting of every node of a tree from start node to leaf node. ,1.0
-3263,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is the traversal of node pointer starting from the root to every other node of tree.,1.0
-3264,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversing each element of the tree is known as tree traversal.\nIts is of 2 types - BFS, DFS.",2.0
-3265,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is a operation in which printout all the data present in tree , there are different type of tree traversal that can be performed. ",2.0
-3266,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","its a method to travel from node to node in a tree data structure, there are condition like going to child or parent.",1.0
-3267,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",it is basically visiting every node exactly once present in the tree,2.0
-3268,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Visiting of each and every element in a tree is called tree traversal. Following are some of the types of tree traversals:-\n1. In order (Left child, Parent, Right Child)\n2. Pre order (Parent, Left Child, Right Child)\n3. Post order (Left Child, Right Child, Parent)",2.5
-3269,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal means traversing and and visiting every node starting from the parent to the root node and then to the child node\n,2.0
-3270,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal"," element. \n1-The pivot can be chosen as the first element or the last element or the middle element.\n2-Next, we divide the array by putting the elements greater than pivot in the right and the elements less than the pivot in the left.\n3- Now this process is continued till we have single elements at last\n4- Now, we combine the elements to form the sorted array.",0.0
-3271,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Quick sort is divide and conquer algorithm in this first we choose the pivot element then we place the pivot element in its correct place and then again do the same with remaining elements,0.0
-3272,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal are the different approaches in which we can iterate/travel through a tree (which can be represented in the form of an array).  The different forms f traversal are pre-order, in-order and post-order and the level-order tree traversal methods. For example in level order tree traversal we traverse the tree in the increasing number of it's levels from left to right.",2.5
-3273,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","traversing a tree means visiting every node of a tree can be BFS traversal, DFS traversal,\nin BFS it refers to breadth first search while DFS refers to depth first search\nin DFS we make stacks and push elements and perform our process, \nin BFS traversal we make queue and push the root of the element and push its left and right child accordingly\nthis way we can access each node of that tree.\n",2.0
-3274,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree is A non Linear Data Structure in which data are stored in nodes\nSo linear Searching Would not be possible to excess all nodes through branches\nSo mainly There are two Traversal \n->BFS(Breadth First Search):Tree is traverse level wise (Require stack to implemented)\n->DFS(Depth First Search):One Brach is traverse followed by next (recursively implemented),2.0
-3275,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal means travelling through the branches to the leaf node or vise versa .,1.0
-3276,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is basically used to traverse the whole tree either first left subtree or right or root node. Three types are inorder , preorder and postorder.",1.0
-3277,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",traversing to the tree from each of its nodes leaf node from top to bottom as per our need,2.0
-3278,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","in this traversal method, the left subtree is visited first, then the root and later the right sub-tree.",1.0
-3279,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Travelling through nodes in a tree is known as tree traversal.,2.0
-3280,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Travelling all the nodes of a tree starting from the root node to all the leaf nodes is called tree traversal. There are basically three types of tree traversals\n1) Inorder traversal\n2) preorder traversal\n3)post order traversal,2.0
-3281,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is traversing in the tree from one node to any other node of the tree.,1.0
-3282,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal refers to iterating over all the elements from the root to leaves using BFS, DFS etc.",1.0
-3283,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal refers to the way we travel a tree. The three types of traversals are In order, Pre Order and Post order. \nIn order:  left->root node->right\nPre Order:  root->left->right\nPost order:  left->right->root",2.0
-3284,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is traversing the tree in 2 forms-\n1.dfs-depth first traversal-uses recursive algorithms\n2.breadth first traversal-uses queue data structure\n,1.0
-3285,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the order in which the nodes are visited/printed for various purposes. Example: Postorder Traversal,2.0
-3286,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Quick sort partitions the array into two parts and sorts the both the partitioned parts hand by hand.,2.0
-3287,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","We go through each and every node of the tree in order to inspect all the nodes of the tree.\n\ninorder - > left, root, right\npreorder - root, left ,right \npostorder - left, right, root",1.0
-3288,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",It is the traversal where each node is traversed in bfs or dfs fashion.,1.0
-3289,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","a form of graph traversal and refers to the process of visiting (like retrieving, updating, or deleting) each node in a tree data structure, exactly once.",2.0
-3290,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",In Quick sort a key element is taken randomly from the array and then it is placed on its true place and then the other elements are sorted accordingly using that element.,1.0
-3291,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the procedures where all the nodes of the tree are visited (traversed).\n\nThere are many kinds of traversal such as:\n1. Pre order\n2. Post order\n3. In-Order,1.0
-3292,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversing a tree means visiting every node in the tree. You might, for instance, want to add all the values in the tree or find the largest one. For all these operations, you will need to visit each node of the tree.",1.0
-3293,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversing the whole tree from root node to the leaf node is tree traversal. We have three methods to traverse:\n\n1. Preorder Traversal - First we traverse root element, then left node and then right node\n2.  Inorder Traversal - First we traverse left node, then root and then right node\n3. Postorder Traversale - First we traverse left node, then right node and then root",2.0
-3294,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","traversing elements in order of the tree. There are three types of tree traversals in-order traversal, pre-order traversal, post-order traversal.",2.0
-3295,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",travelling through each node in a tree is called tree trasveral.,2.0
-3296,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",The method of iterating through a tree to access its elements and perform different operations is called Tree Traversal. ,2.0
-3297,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",linear searching is the an algorithm in which we transverse over the whole data set one by one and find if it a a matching data present in the data set.,1.0
-3298,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","In any given tree, tree traversal is defined as the traversal of every element at all the levels or at every height of the tree.",1.0
-3299,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",,0.0
-3300,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is a procedure in which a root node of a tree is given as input and whole traversal or order of nodes in the tree is returned as output. there are many kinds of traversals like pre-order, post-order, in-order and in-depth traversal",2.0
-3301,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","traversal of element present in trees data structures is called tree traversal . some tree traversal are inorder , preorder ,postorder.",1.0
-3302,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the process of traversing nodes in a tree. Its usually done by traversing child nodes through the parent node.\nThere are three ways of traversal :-\n1)Preorder - Root -> Left -> Right\n2)Inorder - Left -> Root -> Right\n3)Postorder - Left -> Right -> Root,2.0
-3303,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Quick Sort works on the method of partition.\nIt makes partition on the array by selecting a random value and by making the problem smaller and smaller.\nFurthur it rejoins the array and the given array is sorted,1.0
-3304,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal refers to the binary tree or for finding the element in tree to arrange, basically by tree traversal we put number less than Node to the left side of Node and number greater than Node to right side of Node",1.0
-3305,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",traversing through the tree nodes vertically downward by child to child left to left nodes and then right nodes which is called depth first search. \nor traversing all the siblings at the same level which is called breath first search.,2.0
-3306,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in order  traversal\npre order traversal\npost order traversal.,2.0
-3307,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is a go through by an pointer through the whole tree to traverse,1.0
-3308,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is when we travel the tree covering each node and printing them or doing whatever is required. There are mainly 4 types of tree traversal: level order traversal, preorder traversal, inorder traversal, postorder traversal.",1.0
-3309,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal means going to each and every node of the tree.,1.0
-3310,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is the technique to look each node of the tree by means of some methods \nPreorder Traversal\nPostorder Traversal\nInorder Traversal,1.0
-3311,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal means going to each element of the tree and perform some action on it.,1.0
-3312,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is traversing the tree in such a way that all elements of the tree get visited, it can take place by four ways that is preorder traversal, post order traversal, level order traversal and in order traversal",1.0
-3313,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","in quick sort a element called pivot element is selected the that element is assigned its correct position, this divides the array into 2 parts then the element greater than pivot element are on side and element less than pivot element are on the other side. further again quick sort is applied on the two new array formed. ",1.0
-3314,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is the process to visit all its nodes one by one.,1.0
-3315,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is traversing the tree, Can be done in different ways first traversing the parent and then giving priority to their child nodes  or giving priority to the parent then their child nodes ",2.0
-3316,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the process of travelling or accessing all the elements of a tree. There are several ways of traversing a tree like inorder, preorder or postorder.",2.0
-3317,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is a process in which left subtree is visited first ,then the root and then the right subtree.",2.0
-3318,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is simple exploring the branches of the tree one by one or we can say that the left and right child of the tree recursively from the parent node.,1.0
-3319,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",visiting every node in an tree data structure.,1.0
-3320,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","quick sort work as if we have a arrow(cant recall the name), the fixed arrow which helps us to sort the array.",1.0
-3321,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal means moving from the root node towards the leaf nodes following the incoming child nodes.,2.0
-3322,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is traversing all the nodes of the tree from root node to leaf node using different traversals like preorder , postorder traversal, etc. ",2.0
-3323,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal means visiting every node present in the tree till leaf node. ,2.0
-3324,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is visiting each and every node the data structure tree. We can start with the root then the first level nodes, then the second level nodes and so on. We can also start with the leftmost one then its corresponding right node and so on.",2.0
-3325,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is reading the elements of the tree in a certain order.\nIt can be done in preorder, inorder and postorder form.",2.0
-3326,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","The tree traversal is the way of traversing the all nodes of a tree. There are mostly three types of tree traversal which are preorder, post order and inorder traversal.",2.0
-3327,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in tree traversal we move from parent element to its child element and vice versa\neg.  inorder traversal\npreorder traversal\npost order traversal\n\n,2.0
-3328,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the traversing of every node of a tree from root to its children and so on . Its mainly done in three orders Postfix, Prefix, Infix.",2.0
-3329,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",it is the process of traversing every node of the tree from root node to leaf.,2.0
-3330,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",A tree traversal is the process of travelling to each and every node of the tree at least once.,2.0
-3331,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Quick sort partitions the array into two until every element is independent and then it returns the array sorting each element individually.\n,2.0
-3332,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is visiting all the nodes of a tree at least once, in any manner of traversal. ",2.0
-3333,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is when the linked list is represented in a tree form so that the traversal is easy using BFS and  DFS,2.0
-3334,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is visiting each node of the tree once. Multiple techniques exist for the same. Example Inorder, Preorder, Postorder traversal. Also level order traversal. ",2.0
-3335,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Traversing a tree by visiting each vertex of the tree is called tree traversal.,2.0
-3336,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is when we travel from one node of a tree to another node of the tree. ,2.0
-3337,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",It is the technique use to traverse each node of a tree.,2.0
-3338,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.",2.0
-3339,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is traversing through the root node of the tree and moving to left node to get number less than the root node and traversing right to find the element greater than the root node and traversing till you find the root node of the tree to traverse the whole tree.,2.0
-3340,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is the process of traversing through each and every node element of a tree type data structure , in order to look for a particular element or just to sort the tree as per our requirement in the problem statement.",2.0
-3341,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversal means to traverse or to check\\go through all the the nodes in an data structure. We traverse all the data structures to find what we need \nSimilarly we have tree traversal ,we traverse all the nodes of the tree using dfs or bfs to fin the information we need  ",2.0
-3342,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal means the analization of the tree, to travel the whole tree from the parent node to the child and cover each node of the tree.",2.0
-3343,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal means to search and locate a given element or key from the tree.,2.0
-3344,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",,0.0
-3345,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is a process of traversing or visiting all the required nodes(including root and leafs) of a tree with the help of recursion or with the help of iteration.. Here we use the methods like DFS(depth first search using stack or recursion) and BFS(using queues).,2.0
-3346,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",to traverse or visit every node of the tree starting from the root node by some type of algorithm is known as tree traversal,2.0
-3347,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",In tree traversal we traverse all the required nodes in a tree using BFS or DFS.,2.0
-3348,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",The process of reading every data in a tree is called tree traversal.,2.0
-3349,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is way to search the elements of tree data structure . There are different ways for tree traversal like preorder , inorder, postorder tree traversal.",2.0
-3350,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is traverse the each and every node of the tree to search, insert of delete the data from nodes.",2.0
-3351,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal refers to traversing or visiting every node of the tree starting from root node to leaf node,2.0
-3352,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Traversing of all the nodes in a tree is known as tree traversal. There are various tree traversals such as:\n1. Preorder traversal\n2. Inorder Traversal\n3. Post order traversal\n4. Level order Traversal,2.0
-3353,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","It is the visiting of each node in the tree from root node to the leaf node. It explores all the parent to children node. There are various types of tree traversal such \nPre-order traversal , In-order traversal , Post-order traversal ",2.0
-3354,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is a way to travelling the tree from one node to another mainly to find the element.,2.0
-3355,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree. \n\n",2.0
-3356,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is the process of traversing the elements of the tree in specific fashion \nsuch as inorder (root,left,right),preorder,post orderd",2.0
-3357,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is to visit every node of an tree mainly of 4 types\npre order\npost order\n in order\nLevel order \n,2.0
-3358,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is to traverse the tree by first taking the root node and then by taking the left node and then by taking the right node and applying recursion for the left and right node to fully traverse the tree,2.0
-3359,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Insertion sort performs sorting in a different way as compared to selection sort. It uses more time and is in general less efficient to sort as compared to selection sort. Insertion sort usually does it in O(n^2) time while selection sort in some cases can perform the same in O(nlogn). ,2.0
-3360,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","It is a method in which the left subtree is visited first, then the root and later the right sub-tree and  every node may represent a subtree itself. If a binary tree is traversed in-order, the output will produce sorted key values in an ascending order.",2.0
-3361,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree Traversal -  when we have the Tree Data Structure(might be Binary - Full, Complete, etc.). Each node has children which further serve as nodes until we get the leaves - nodes with no children. So now we have a data structure with edges that is connecting two data values in the form of nodes. So when we want to perform any simple operation on the data structure like searching, etc. We go through all the nodes while traversing through the edges ( connections). This is known as tree traversals.",2.0
-3362,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is technique to traverse/cover each element of a tree.\nThere are various methods to traverse a tree:\n1) Postorder\n2) Preorder\n3) Inorder\n4) BFS\n5) DFS,2.0
-3363,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the technique to retrieve information from a tree, This can of different types like:\nIn-order, pre-order, level-order, post-order etc.  ",2.0
-3364,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is a way to traverse a tree and visit the elements present in the tree.\nIt is of three types:\nPreorder tree traversal:In this kind of traversal,the root node is visited first and then the left and then the right node.\nPostorder tree traversal :In this kind,the left node is visited first,then the right and then at the end the root node is visited.\nInorder tree traversal:In this kind,the left node is visited first,then the root node and then the right node.",2.0
-3365,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","It is the process of gathering information from a tree by traversing a tree to perform certain operation.\neg: In order traversal ,pre order traversal and post order traversal.",2.0
-3366,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Traversing a tree means moving from the root node to the leaf nodes without missing a single node.\nIt needs to look through all the nodes. Used in searching a tree, inserting a new element, ",2.0
-3367,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is a way to traverse every node of the tree from root node to the leaf node,2.0
-3368,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",traversing a tree and collecting information to do certain operations,2.0
-3369,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",,0.0
-3370,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in tree traversal we traverse the node of the tree from the root node to the element we want to search or to the leaf node. mostly we use recursion to traverse the tree.,1.0
-3371,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is a technique through which we traverse the tree formed using arrays i.e. we visit the nodes of the tree and perform the desired operation.,1.0
-3372,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","it is a way of exploring different branches/nodes/elements of the tree .we have different types of tree traversal,like  level order traversal,pre order traversal,post order traversal,in order traversal,etc..which can be used according to the problem .",2.5
-3373,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tress traversal refers to a way in which we go through the elements of a tree one by one, it can be dfs(depth first search) and bfs(breadth first search). In dfs, we traverse the tree by going down or up (depth) whereas in bfs we go left or right (breadth)",2.5
-3374,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","In tree traversal we go element to element in tree by many methods like - bfs, dfs, inorder. ",2.5
-3375,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in the tree traversal we reach the every node of the tree and if want to print by the print function also.,2.5
-3376,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","the manner in which a tree is traversed is called tree traversal. it is of three types, in-order, pre-order, post-order",2.0
-3377,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the process of travelling nodes of tree, that is starting from the root node to the leaf node. The direction of tree traversal can depend upon the use cases. ",2.5
-3378,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal refers to visiting each node of a tree. for a binary tree there are three types of traversals:\n1. Preorder\n2. Inorder\n3. Postorder,2.5
-3379,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Visiting of every node of tree is refferd as tree traversal.,2.5
-3380,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal basically means how we move in a certain tree which has a root and leaves thereare some ways to traverse trees like DFS and BFS,2.0
-3381,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is a type of techinque which used to trsvel a tree data structure according to the problem there are many types of traversal like bfs,dfs etc",1.0
-3382,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Going from root node to different leaf nodes is called tree traversal.,1.5
-3383,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is a method to traverse a tree in order to visit each and every node and do certain operations accordingly. Tree traversal can be pre order, post order, in order traversal. In in order-:Left, root , right. Pre order-:root->left->right and post order->left->right->root.",2.5
-3384,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in tree treversal we generally traverse each node and to do this there are certain methods like in one case we traverse the root first then the left side and then the right side,2.5
-3385,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",it is way to visit each element of the tree data structure for purpose of different operations such as searching. there are many types of tree traversal such as level order etc,2.5
-3386,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is a method to traverse or cover a tree. In a binary tree, traversal is done considering two sides of a tree through pointers namely right and left. Using the basic binary tree property of numbers greater than node on right and lesser on left, decision can be taken as to which side the process needs to be done.",2.0
-3387,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Traversing each or some node of a tree is known as tree traversal. Generally there are two algorithms utilized for tree traversal BFS (Breadth First Search) and DFS(Depth First Search).\nDFS take stack into account whereas BFS takes queues into account for the traversal.,1.5
-3388,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in which we start from root node and then different leaf node and traverse in a levels ,2.5
-3389,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","It refers to traversing through all the nodes of the tree. Preorder, inorder and postorder are different ways of tree traversal.",2.5
-3390,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is visiting  every node of the tree \nfor eg;\nbfs(breadth first search)\ndfs(depth first search)\n,2.5
-3391,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","in this sub tree is visited first, then the root tree and then right sub tree ",2.0
-3392,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",traversing the tree from parent node to least child node or least child node to parent node \ntwo ways are:- BFS (breadth first search ) and DFS (depth first search),1.5
-3393,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree taversal means traversing the whole tree starting from the root of the tree till last leaf node of the tree . There are three methods for tree traversal :-\nInorder , Preorder , Postorder.",2.5
-3394,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tracing the Tree form root to leaf node is known as tree traversal.,2.5
-3395,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traverse is the operation of traversing or follow the path of tree to get its leaf node or whatever be the aim of our problem.,2.5
-3396,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in tree we have a parent node and to each parent node have a child node connected to it .\nto find any element we travel through a parent node to most right and and search left after that this operation runs until we reach to the end node,1.0
-3397,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the visiting of every nodes/elements of the tree one by one through several different techniques like BFS(Breath First Search), DFS(Depth First Search), etc.",2.5
-3398,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is going on every node of tree from root.\n we have 3 type of tree traversal inorder , preorder, post order",2.5
-3399,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",,0.0
-3400,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is a process in which read or visit each and every node of the tree,2.5
-3401,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal reffers to the graph traversal algorithm in which each element is being traversed and arrange in the form of tree,0.0
-3402,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Traversing whole tree from root node to Leaf node. Thera 3 types of traversals \na) Inorder b) Post order c) Pre order,2.5
-3403,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal is a form of searching we do on tree data structure where we use diffrent types of traversal methods like postorder,inorder, preorder to search for an element.",1.0
-3404,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",in tree traversal we identify the parent and the child nodes of the tree according to the requirement traversal is started from root node and then reached to parent ,0.0
-3405,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",,0.0
-3406,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","Tree traversal is the process of traversing or going through the tree data structure to know which elements are present at which position or node of the tree. There are three forms of traversal which are pre-order , in - order and post - order traversal",2.0
-3407,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",Tree traversal is define as searching of an element ,0.0
-3408,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",it is when we start from the parent node of tree and come to end root by following a path,0.0
-3409,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","the traversal of elements , in end to start to start to end in right way in tree form , known as tree traversal",0.0
-3410,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","traversing the tree is going from root node to every other node in the tree.\neg inorder, preorder and postorder.",2.5
-3411,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal mean we go to each node of that given tree then we can say this tree is travers their are 2 main method of doing this BFS , DFS.",2.5
-3412,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","It is a method of tracing a tree from one node to another.\ntree traversal are of 3 type - inorder, preorder, and post order traversal.",2.5
-3413,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",tree traversal is we move one node to another node is tree traversal,2.0
-3414,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",bfs & dfs.,0.5
-3415,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal",traversing a tree from the root node to all the leaf nodes in a tree ,1.0
-3416,What is tree traversal?,"Tree traversal is a process to visit all the nodes of a tree. Because, all nodes are connected via edges (links) we always start from the root (head) node. There are three ways which we use to traverse a tree − (1) In-order Traversal (2) Pre-order Traversal (3) Post-order Traversal","tree traversal means, if the tree is given, we have to traverse the tree up to bottom.",2.0
-3417,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3418,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is the tree in which all the nodes are balanced i.e the height of left subtree and right subtree should be equal so all nodes should be balanced then only it is called as an AVL tree,2.5
-3419,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"avl tree is a self balancing binary tree ,a binary tree is a tree where the node can have 2 or 0 childs ",1.0
-3420,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL tree inserts a new value in a binary tree by comparing if it is smaller or larger than the parent, thus making it easy for traversal ",1.0
-3421,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is a type of tree. Its node has a value called balance factor. Balance factor is the mod of number of nodes on left of a node - number of nodes on right side.\nThis balance factor cannot be greater than 1 at any instant. To maintain the balance factor the AVL tree has rotations which are done when there is an imbalance on any node. This helps as there can be certain cases in Binary search tree where the depth of the tree is too large and the data is stored inefficiently but rotations in AVL tree allow us to maintain the height by doing rotations hence ensuring a efficient storage of data in tree.,1.0
-3422,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a height balanced tree ,it means |height of left part-right part at every node| should be <=1.\nTo balance the tree we rotate the tree in RHS or LHS.",1.0
-3423,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL is an another balanced binary sub tree.\n AVL stands for adelson velsskii and landis ,1.0
-3424,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a sub form of binary tree. In which left  node is less than right node.,1.0
-3425,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is type of binary tree in which left node is always less than right node \nit uses rotation (left and right) to maintain the property of AVL tree,1.0
-3426,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),an AVL tree is a self-balancing binary search tree.,1.0
-3427,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL is a binary tree which build the tree  using functions like LR shift ,RL shift , LLR shift ,RRL shift .",1.0
-3428,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is form of binary tress that ensure that heavy element should be stored in the left of the tree and light element at the right side of the tree. AVL tress can be achieved by using operations like LEFT-LEFT , LEFT-RIGHT,RIGHT-LEFT,RIGHT-RIGHT rotation and it reduced the time for the purpose of searching.",1.0
-3429,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a tree which at least consist of two children or one, and it is a form of binary tree.",1.0
-3430,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A balanced binary tree which maintains the weight of children on both its left and right side is called an AVL tree. The ratio of depth or right and left sides never exceeds 1.,1.0
-3431,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),a tree which allows rotation on the basis of insertion and has a height equal to the greater number of children of both the sides.,1.0
-3432,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"It is a type binary search tree with some condition :- \nFor every node\n| Height of left sub tree - Height of right sub tree |  = [-1, 0, 1]\nTo maintain such condition we have to perform some rotation in regular binary search tree. ",1.0
-3433,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is a self height balancing search tree.\n\nProperties of AVL tree:\n1.All the nodes on the left of a node are smaller than the current node\n2. all the nodes on the right of the current node are larger than the current node\n3. Mod of the difference in height of the left subtree and right subtree of the root node is always less than or equal to 1,1.0
-3434,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Tree traversal is a technique or a way to read a given tree and get the respective values or the information which it is carrying.,1.0
-3435,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Avl tree is the balanced binary tree.\nit has all the small elements than root on the left and all bigger elements than root on right.\nthe minimum distance from root to child must be maintained .,1.0
-3436,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3437,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a tree where the left subtree of the root has nodes smaller than it and right subtree has nodes bigger than it and this pattern is followed in each subtree of the AVL tree.\nIn an AVL tree the difference of height between left subtree and right subtree has to be less than 2 in every position and this rule cannot be broken.\nIt is a balanced tree,2.5
-3438,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An avl tree is a binary tree where the left subtree of the root  has nodes smaller than it and right subtreee has nodes larger than it and this pattern is followed in each subtree of the avl tree.Also in an avl tree the difference between the height of subtrees is not larger than 1,2.0
-3439,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a type of tree data structure in which the nodes are arranged in order where the smaller data is placed on left side of the parent node and the greater to the right side thus making it easy for traversals of node as arranged in increasing manner. ,0.0
-3440,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Tree is a non-linear data structure. Traversal of tree refers to the different ways in which each node of a tree can be visited. There are three types of traversals:\ni) inorder traversal           [left-node-right]\nii)preorder traversal         [node-left-right]\niii) postorder traversal     [left-right-node],2.5
-3441,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL is balanced binary searched tree , where each node have smaller element in their right side and bigger element in left side .",1.5
-3442,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL tree is a sorted Binary Search Tree in which each parent has at most two nodes and height on the left and right side is balanced, which makes searching and other algorithms work efficiently.",2.0
-3443,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a type of Binary Search Tree with balanced height in both left and right sub-trees.,2.5
-3444,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is a balanced Binary Search Tree.,1.5
-3445,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A balanced binary tree is called AVL tree.,1.5
-3446,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"In AVL Tree, a node is inserted to the left if it is smaller than the current node or its right if it is equal or larger. Then, the balancing factor of all the nodes of that half of tree is checked. If it is greater than 1 or less than 1, then a rotation of nodes is made to make the tree balanced. ",2.5
-3447,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),a balanced binary tree is called AVL tree,1.5
-3448,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),special type of binary tree where for node\n|left height -right height|<=1,2.5
-3449,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a self balancing binary tree which balances itself after insertion of each node where height of each node can be -1,0,1 .As soon as height of tree or subtree gets 2 or -2 ,tree balances itself .",2.5
-3450,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A balanced binary tree is called AVL tree,1.5
-3451,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3452,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree refers to a balanced tree in which heights of the two subtrees of any node can only differ by 1.,1.0
-3453,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is the one in which the max difference between left and right subtree could be 1.,1.0
-3454,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),The tree which have a structure based on the branch weights of the root and child-nodes of the tree is called an AVL tree. In AVL tree the smaller elements should be at the left and the bigger elements should be at the right if the tree. AVL also consists of many rotations through the process of making it.,1.0
-3455,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"In quick sort, first we divide the array into two parts and perform selection sort on it and them combining it while comparing all elements to form a sorted array",1.0
-3456,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),an avl tree is a balanced binary search tree in which the height is restricted to (logn) instead of (n).,1.0
-3457,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL tree is a binary tree in which the value in every left node should be less than the corresponding parent node, and the value in every right node should be greater than the corresponding parent node.",1.0
-3458,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A balanced binary search tree where the difference of depth on the right subtree-left subtree does not exceed 1 is called AVL tree. ,1.0
-3459,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a data structure which a bit less efficient when compared to red black tree. it is a balanced search tree, left and rifts structure are always equal",2.5
-3460,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"In Tree Traversal, each element is visited from root to leaves. In order, to find some element or maybe to calculate the prefix, postfix, infix.",1.0
-3461,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An array tree where there is a limit of sub node and the elements which are lesser than the root node at each level are put to the left of it and the nodes greater to the root node are put to the right of the sub node. ,1.0
-3462,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a improved form of data-structure called trees, it includes the concept of rotation on the basis of elements order. The aim is to build a tree which follows the property of perfect binary tree. ",1.0
-3463,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL is Binary Search tree that has same number of leaf nodes attach to its parent nodes that it is balanced binary search tree.,1.0
-3464,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A balanced binary tree which has all balanced leaf nodes is called an AVL tree.,1.0
-3465,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL Tree is a balanced binary tree in which all the leaf nodes are balanced.,1.0
-3466,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A balanced tree in which all the leaf nodes are balanced is known as AVL tree.,1.0
-3467,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tress is a type of data structure that is used to store information in kind of a tree having root and leaf. It stores data in a manner that left child contains smaller element and right child contains greater element. It is also known as self balancing tree as it performs rotation whenever there is disbalance. It performs rotations like Left left,right right,left right and right left rotations based on the disbalance happened. Disbalance in AVL tree is counted by subtracting number of left child from right child and if it is greater tha equal to 2 then we perform rotation.",1.0
-3468,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a binary tree in which difference of height of of left child and right child is at most 1.,1.0
-3469,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a self-balancing binary search tree that maintains a height difference of at most one between its left and right subtrees. It was the first such data structure to be invented and guarantees a worst-case time complexity of O(log n) for insertion, deletion, and search operations.",1.0
-3470,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),It is a self balancing binary search tree which has rotations involved for its nodes.,1.0
-3471,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is BST tree in which the nodes are weighted based on their position and a variable is maintained which if becomes greater than 2 or smaller than -2 the tree becomes unbalanced and we need to balance the tree by rotating.,1.0
-3472,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a binary tree in which the height of left and right children cannot differ by more than 1,1.0
-3473,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree are the self balancing trees which uses 0 and 1 as a balancing factor and via LL,RR,LR,RL combinations the tree balanced itself ",1.0
-3474,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a type of balanced binary tree. Each node of this tree is associated with a balance factor. If the balancing factor is 2 or more than 2, then it do rotations to make it balanced.  ",1.0
-3475,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Avl tree is a self balancing tree. It is a binary search tree which requires less swapping for balancing,1.0
-3476,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),It is a balanced tree .,1.0
-3477,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"it is a binary tree that balances itself , it has balance factor which can be -1,0,1",1.0
-3478,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL can be defined as the balanced binary search tree,1.0
-3479,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL is special type of tree where searching is easy and insertion take lots of time,1.0
-3480,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL tree is a balanced binary search tree where there are parent and child nodes. If the value f the child node is smaller than the value of the parent node then it will be on the left side of the parent node or else it will be on the right. Also the difference between the number of nodes on left and right should be either -1, 0 or 1. If the difference is anything other than these then appropriate rotations are made to meet the requirements.",1.0
-3481,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"A AVL tree is a  binary search tree where elements are inserted in such a manner that the difference in the height of the level of node inserted and any other node does not becomes anything other than{-1,0,1} and if the difference becomes something else we use LL , RR. LR , RL rotations.",1.0
-3482,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a type of tree like red black and binary that rotates the values when one side of the tree has greater value than the other (left for smaller and right for bigger values),1.0
-3483,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a type of binary tree where leaf node must  has 0 or 2 children.,1.0
-3484,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL tree is a self-balancing binary search tree. In an AVL tree, the difference between heights of left and right subtrees cannot be more than one for all nodes 2. The AVL tree are useful to get all basic operations done in O(log n) ^2.In the avl tree the sum is not more than 2  and occur more than 2 than other opearation can be performed.",1.0
-3485,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"it is a height balanced binary search tree. where the height difference of (left -right) of a root node is 1, -1 ,0",1.0
-3486,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL Tree is a type of a balanced binary tree where we use rotations to keep the tree balanced. Each node has a value attached to it : (right branch - left branch) which should always be -1, 0 or 1. Should it be more or less than that, we rotate the tree from the unbalanced node until the property is fulfilled once again.",1.0
-3487,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL is a self balancing tree in which the height difference between the left and right child of any node is less than or equal to 1, and in which left child is smaller than the parent and right child is greater than the parent.",1.0
-3488,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"avl tree is tree based data structure in which it is height balancing, each node has it's own balancing factor to perform ll,rr,lr,rl shifts",1.0
-3489,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a balanced binary tree in which each node has a balancing factor of 1/-1/0.\nIf on insertion the balancing factor gets disturbed then the graph undergoes either of the following rotations to retain the balance factor:\n1.LL\n2.LR\n3.RR\n4.RL,1.0
-3490,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a category of trees in programming where the no. of elements or we can say that the level of the tree on both the sides of its branches is equal, it is basically a balanced binary tree or a balanced k-ary tree.",1.0
-3491,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a self balancing tree. After inserting every node we first check if the tree is balanced or not.,1.0
-3492,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is nothing but a balanced binary search tree.,1.0
-3493,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Avl trees are trees that are arranged in accordance to the weight of each node which is calculated by type number of nodes that tree has. The element having less weight are placed on the left hand side of the tree while the elements having higher weight then the root of the sub tree are placed on the right hand side of the leaf node. In this manner an AVL tree is generated. Later with the addition of new nodes the avl tree is readjusted so that the weight of the left hand side and right hand side does not come out to be more than 2.,1.0
-3494,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"avl tree is a tree in which every node has a variable which is the difference of the number of nodes  of the left tree - the number of nodes in the right subtree \nto maintain the property of avl tree, every node should be balanced ( the value of diff variable can be -1,0,1) if not balancing is maintained by balancing techniques.  ",1.0
-3495,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a self-balancing Binary Search Tree where the difference between heights of left and right subtrees for any node cannot be more than one.,1.0
-3496,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a balanced tree. it rotates on its own o maintain the balance when new nodes are added,1.0
-3497,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is the arrangement of the tree on the  bases of balancing factor according to trhat we make the right rotation and left rotation in it ,1.0
-3498,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree as a self balancing tree in which it performs some rotations so that the difference between the height of a left sub tree and right sub tree is at most 1,1.0
-3499,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is the balanced tree ,height of ech node is balanced and the height cannot extend from absolute 1.",1.0
-3500,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is a self balancing Binary Search Tree.,1.0
-3501,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is a binary search tree where small numbers are present on the left side and larger numbers are present on the right side of the root node and the balancing factor of every node is between -1 to 1,1.0
-3502,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Is a balanced binary tree which has a bf (balancing factor) for each node that is the length of right side - length of left side. ,1.0
-3503,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),an avl tree is a binary tree with the height on both of its subtrees balanced .It is a more efficient implementation of binary tree for searching an element in it.,1.0
-3504,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree), AVL Tree is a balanced binary tree while inserting the nodes we keep the check on the balanced factor and accordingly insert the nodes.,1.0
-3505,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL TREE is a tree where maximum height of tree should be 1 .,1.0
-3506,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is self-balancing binary search tree that can perform certain operation in logarithmic time.,1.0
-3507,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is the tree whose root's balanced height is {-1,0,1} . Balanced height of the node is = height of left child-height of right child.",1.0
-3508,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL Tree is a Binary Search Tree in which the balance of each node is either -1,0,1",1.0
-3509,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree are the tree in which the total node value of left child is equal to total node value of right child , if we insert any extra node we perform necessary rotations to equal the node value of each child.",1.0
-3510,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"a tree where every node is balanced that is every node has balance 1,0,-1.",1.0
-3511,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"it is a height balanced tree it has a balance factor which can take value -1,0,+1 this balance factor help to maintain the height of left and right subtree\nit perform searching in (nlog(n))",1.0
-3512,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a self-balancing binary tree.,1.0
-3513,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"avl tree is also named after a balancing factor tree where we balance the parent and the root node. balancing factors can only be -1,0,1",1.0
-3514,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"Visiting the various nodes in a tree is called tree traversal, there are different methods:\n1-InOrder\n2-PreOrder\n3-PostOrder\n4-DFS",1.0
-3515,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"We start from root node then traverse each and every node one by one untill we reach the leaf node . There are three types of tree traversal postorder , preorder and inorder",1.0
-3516,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL is a self balancing binary tree where the tree is balanced from all of the child nodes of the root. The value of all child elements to the left are less than the value of the parent and the value of all the child elements to the right is greater than that of the parent. To keep the AVL balanced there are different rotations performed when an element is added to the the tree such as the LL, RL, LR, RR rotations.",1.0
-3517,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"the value of the right child is greater than the value of the root\nvalue of the left child is lesser than the value of the root\nhere these properties are of AVL trees, using this we tend to reduce our complexity while traversing and make our program efficient.",1.0
-3518,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is  a binary Tree in which right children  have bigger value then left children \nand tree is constructed by following certain complex methods.\n,2.0
-3519,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),a binary search tree which has one parent node its leaf nodes and most importantly its left branch values should be less than parent and parent value should be less than right node..,2.0
-3520,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Avl tree can be defind as a height balanced binary search tree in which each node is associated with a balance factor which is calculated by subtracted the height of the left and right subtress for any node.,2.0
-3521,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),height difference is equals to +-1,1.0
-3522,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL tree is a balanced binary search tree. Like red-black trees, they are not perfectly balanced.",1.0
-3523,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"It is a balanced binary tree where the difference in height of left and right subtree is 0,-1,and 1.",2.0
-3524,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a height balanced tree in which the difference between left subtree and right subtree cannot be greater than 1. We can always make a tree into an AVL tree by performing insertion and deletion operations.,1.0
-3525,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a height balanced binary tree in which, at any node the difference of height of the left subtree and right subtree is 1 or less than 1. ",1.0
-3526,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL is a self balancing tree which balances itself on the basis of weight(the difference of weight of the sub tree can only be b/w 0 and 2),1.0
-3527,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a tree formed with rotations (left, right) according to the data stored in the node. Formation of AVL tree follows a specific set of rules. ",1.0
-3528,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a self balancing binary search tree that maintains its height balance .\nthe height of left and right subtree differ by at most 1,2.0
-3529,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL Tree is a self re-arranging tree and it compares the height of the tree sides while accepting -1,1 and 0 as the values. If in case a discrepancy is faced between the right and left nodes, they are rearranged.",1.0
-3530,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Visiting each and every node of the tree once is called traversal.There are three types of traversal- 1.inorder 2.postorder 3.preorder,1.0
-3531,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree tries to minimize the gap between the root and leaf nodes for it being equal to 2. ,2.0
-3532,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is height balanced BST in which each node is associated with a balance factor which is calculated by subtracting the height of its right sub-tree from that of its left sub-tree. it is said to be balanced if balance factor of each node is in between -1 to 1  else the tree will be unbalanced and need to be balanced.\n\n,2.0
-3533,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a balanced binary search tree in which each element has a balance factor which is difference between height of left subtree and right subtree and balance factor is less than equal to 1,2.0
-3534,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"Tre traversal refers to the process of traversing the tree from root node and then left to right or vise versa. Some traversals are preorder ,level order traversal ,postorder traversal.",2.0
-3535,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL trees are the type of trees that can make their structures more search-efficient (reduce time complexity of search) upon insertion of an element by modifying it's height.,2.0
-3536,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a self-balancing Binary Search Tree (BST) where the difference between the heights of left and right subtrees of any node cannot be more than one.,2.0
-3537,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a type of tree in which the difference between the height of left subtree and height of right subtree should be -1 or 0 or 1. We need to check the height in each and every step.,1.0
-3538,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a binary search tree in which the difference between the difference of node and left child and the difference of node and right child can be 0 ,1 or -1.",1.0
-3539,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree \nit compares height\nonly accepts values -1,0,1\ntries to minimize the gap between the root and leaf node.\nwe need to perform rotations to solve AVl tree",2.0
-3540,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL trees are those which have a height difference of +-1 between their left and right subtrees.,1.0
-3541,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"in insertion sort we basically pick and arrange the element according to their neighbor elements, whereas in selection sort we find the minimum element and then find exactly real position of the element and place it there.",1.0
-3542,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a self balancing binary search tree. ,2.0
-3543,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"an AVL tree is a self-balancing binary search tree. The heights of the two child subtrees of any node differ by at most one; if at any time they differ by more than one, rebalancing is done to restore this proper. ",2.0
-3544,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),an AVL tree is a binary tree which can modify its height to the least height possible for the binary tree to exist to make its structure more efficient for searching in terms of time complexity after insertion of an element in the tree,1.0
-3545,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3546,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a self balancing binary search tree(b.s.t). Its named after the initials of its designers.,2.0
-3547,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Tree Traversal are the methods to traverse (or visit) the whole tree. Some of the methods of tree traversal are-\n1.) Inorder\n2.) Preorder\n3.) Postorder\n4.) Level-Order,1.0
-3548,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3549,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"Avl tree is a sorted tree which has the biggest element at the top and all the arranged nodes are sorted at the input only, it can also rotate \nright to left or left to right , and even left to left and right to right if there is a colision of nodes and the order and level is disturbed.\nsmaller element goes to the ;eft and the bigger one goes to the right of the parent node.",1.0
-3550,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is right side tree big roots and left side small root,2.0
-3551,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a balanced binary searched tree after red black binary searched tree,1.0
-3552,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a height balanced binary tree in which at any point the difference of the height of the left subtree and that of the right subtree is never greater than 1 or less than -1. ,1.0
-3553,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a tree in which we rotate the element of the tree if the condition is not fulfilled. In this tree the maximum element is the parent node and node attach to it is known as child note,1.0
-3554,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL trees are the binary trees in which height is balanced of each and every node,2.0
-3555,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a type of tree in which difference of height on both branches connected to a node is 1 ,0 or -1.",1.0
-3556,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is a balanced binary search tree in which elements smaller than root are left children of tree and elements greater than root are right children of tree,2.0
-3557,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),tree traversal means checking each element of the tree ,2.0
-3558,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is a kind of binary search tree that arranges nodes of root node by checking a formula of no of left child -no of right child,1.0
-3559,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,
-3560,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is the one in which there is a maximum difference of 1 between the left and the branch from any particular node.,2.0
-3561,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is a balanced binary search tree. Each node has an attached balanced factor whose value must not exceed 1. Balanced factor is : |height of left subtree - height of right subtree|.,1.0
-3562,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree are the height balanced trees in which the difference of height of the left side and right side of any node is at most 1.,2.0
-3563,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),the balanced binary search tree in which we also maintain height at each node.,1.0
-3564,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"Tree traversal refer to traverse within a tree, to travel all the nodes present in the tree. \nFor example in case of binary traversal is to travel each and every node of tree that is to travel all nodes to left of root of the tree and all nodes to the right of root of the tree.",2.0
-3565,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a self balancing tree where the weight of a node can be either 0 or 1 or -1, avl trees tend to reduce the overall height of the tree which helps in reducing the search time of the trees.",1.0
-3566,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is balanced binary tree where the elements less than the root node is on the left side of the root node while greater than the root node lies on the right side of the root node. And the tree is made also using different rotations. ,2.0
-3567,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a height balanced tree as in this the height of left and right tree is almost same .,2.0
-3568,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is a tree where the right node should be greater than the left node in a binary tree.,2.0
-3569,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"an avl tree is a tree which is properly balanced tree.\nthe height difference of the left and right subtree of every node is -1,0,1 only.",2.0
-3570,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is the tree which is also known as self balanced tree , it uses the concept of rotation to store the elements and hence it balance the height of the tree. This method improves huge time complexity as compared to the simple tree.",2.0
-3571,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),an avl tree is a balanced binary search tree used to store data in which we use rotations to store data,2.0
-3572,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a tree where there's a maximum difference of 1 between the number of child at left node and number of child at right node.\nif the difference becomes greater than 1 then there is shifting between the nodes of the tree to re establish the unit difference.,2.0
-3573,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a kind of binary tree which is balanced.,2.0
-3574,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree are the efficient form of tree building. ,2.0
-3575,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Tree traversal is a way of printing a tree either horizontally or vertically by either using Breadth First Search (BFS) or Depth First Search(DFS) respectively.,2.0
-3576,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A Avl tree is the one in which the difference in the height of the left and right tree cannot exceed +1 or -1.,2.0
-3577,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL trees are a method of traversing a tree its ful name is avial variation tree.,2.0
-3578,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is a height balanced tree. With insertion or deletion the tree automatically balances it's height. The effectiveness comes in when we use operations like searching as the tree is not skewed such operations later on become faster. ,2.0
-3579,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a binary search tree, in which rotations are made in order to sort it.",2.0
-3580,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a height balanced binary tree in which at any point the difference in height of left sub tree to right sub tree is 1 or less than 1.,2.0
-3581,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),It is a binary search tree in which rotations are made to sort the tree.,2.0
-3582,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a Balanced Binary Tree in which the left node is less than the parent node and right node is greater than the parent node,2.0
-3583,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),In Avl tree the lower elements are placed in the left side of the root node and higher elements are placed on the right side of the root node following this throughout the traversal till at the end root nodes are found.,2.0
-3584,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3585,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is an sorting algorithm which helps to sort the elements or bits according to the algorithm of AVL tree,2.0
-3586,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3587,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is a binary search tree which is balanced by height where each node is given a balance factor.,2.0
-3588,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),It is a balanced binary searched tree like red black tree.,2.0
-3589,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"avl tree is a self balancing binary search tree. It was first data structure to be invented. In avl tree, the height of the two child subtrees of any node differ by at most one.",2.0
-3590,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL is balanced binary tree in which the balancing factor can be only -1,1,0",2.0
-3591,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a balanced binary tree in which the difference in heights of the two subtrees is -1, 0 or 1.",2.0
-3592,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a self balancing binary search tree,2.0
-3593,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An AVL tree is a type of binary search tree which follow a particular rule . The rule is that the absolute difference of the height of the right subtree and the left subtree  of any node should not be greater than 1 ie the difference can be -1,0,1.  If conditions are not met we perform rotation like ll,rr,lr,rl rotations to make an avl tree.",2.0
-3594,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree is self-balancing Binary Search Tree . where the difference between heights of left and right subtrees for any node cannot be more than one.,2.0
-3595,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a type of balanced search tree in which difference in the height of the two subtrees is less than 2 i.e it is always -1,0,1.",2.0
-3596,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a modified version of Binary search tree (BST) , in AVL tree all the nodes having value less than root node appers to be in left of root and bigger than root is places in right of root and this is followed for all the nodes, also the difference in heights of left sub tree and right sub tree is not greater than one.",2.0
-3597,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),It is the type of Tree in which the weight of the nodes are compared . \nThe child which is at the left side will have less weight than the parent node while the child which is at right side will have greater weight than parent node.\nThe tree which satisfies this condition is known as AVL tree.,2.0
-3598,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is a tree in which left child of a node is smaller than the node and right child is greater than the node.,2.0
-3599,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a self balancing binary search tree.,2.0
-3600,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),an avl tree is a self balancing binary search tree which maintains its height log n(n is the number of nodes) after every operation performed on it\nall elements that have less value than the value in the node are in left subtree and all the elements with more value in right subtree\nit is an advanced version of binary search tree,2.0
-3601,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Avl tree are the balanced trees that means\nexcept leaf nodes every node have two child ,2.0
-3602,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),self balancing binary search tree in which every node is assosicated with the balance factor,1.0
-3603,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"Quick sort works by making partitions in any data structure, say it is an array, and then the array is partitioned in half. It works on divide and conquer approach so it keeps on making multiple partitions until it is efficient enough such that we can then sort the partitioned array in the most efficient manner and then re-join it such that the whole array is sorted towards the end. It takes a time complexity of O(nlogn). ",1.0
-3604,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree)," AVL tree is a self-balancing binary search tree.  In an AVL tree, the heights of the two child subtrees of any node differ by at most one; if at any time they differ by more than one, rebalancing is done to restore this property.",1.0
-3605,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"A special form of Binary tree in which rotation happens itself at the insertion of nodes based on the evaluating criteria of the AVL. Which is - If the left side nodes imbalances the right side nodes, the rotation happens in a way that the tree balances itself.",1.0
-3606,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a binary tree in which the difference of the height of the left child and the right child  is 1.,1.0
-3607,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is the modification of binary search tree in which balance of tree is also stored if their is any in-balance we do  operations like l-r, r-r, l-l, r-l rotations depending on the situation.",1.0
-3608,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL tree ,1.0
-3609,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"It is a kind of Binary Search Tree which is formed by comparing the balance factor and different operation like [LL ,RR ,RL and LR ]rotation are done depending upon the balance factor.",1.0
-3610,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3611,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree), AVL tree is type of binary search tree in which whenever a new node is inserted at the end it checks with its parent it is at right position then no problem but if the element is not at right position then different rotations take place left or right to make the tree binary search tree in which all the elements less than parent are on left side and greater than are on the right side,2.0
-3612,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),type of BST (binary search tree)\nfour types of rotations can be performed depending on the balance factor:-\n1)LL or left-left\n2)RR or right-right\n3)RL or right-left\n4)LR or left-right,1.0
-3613,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),A pivot element is chosen from a group of elements. Other elements are placed on the right or left of the pivot element depending on whether they are larger or smaller than the pivot element.,1.0
-3614,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a self balancing binary tree in which element which is greater than its parent is placed at right and element less than its parent is placed at left of the parent,1.0
-3615,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a self-balanced binary tree in which the elements greater than the root are placed in the right and less than it are placed on the left.,1.0
-3616,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a type of self-balancing tree (data structure),2.5
-3617,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL trees are those in which all nodes are balanced. The element of lower weight should be on the left node of a tree and the greater one should be on the right.,1.0
-3618,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),The AVL tree is optimise verson of binary tree in which the difference between least and maximum level is not more the 2.\n,2.0
-3619,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),in the avl tree the upper node that is first node of the tree(parent) is larger than the left child and lesser than the right child.also it for the left chind as parent that it is grearter than of the left ching and lesser than the right child and so further for the right node and so on.,0.0
-3620,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"it is an example of balanced tree, it is balanced by appropriate rotations whenever a new element is inserted.",2.5
-3621,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL Tree is an binary tree, where each parent node has two children node. Also we create AVL tree such that left node is always smaller than the parent node and right node is always bigger than the parent node.",1.0
-3622,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL trees are height balanced Binary trees in which the difference of heights for each node is either +1, -1 or 0",2.5
-3623,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is the binary tree having smaller elements than root to left side and the elements on the right side are greater . If element is greater the the parent node the child gets attached to the left side if child node is smaller the the parent node its gets attached to right side.,1.0
-3624,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),It is a Balanced Binary tree ,2.5
-3625,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3626,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3627,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a type of  tree in which left and right rotations are done whenver the condition of tree is not met. Condiions->right of root shoulod be greater and left of root should be smaller.,1.0
-3628,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is a special tree in which the maximum element is on the right most part of the tree and the least element is on the left most part,0.0
-3629,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a self balancing tree which balances its height at each level after operations such as insertion and deletion,2.5
-3630,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is an abbreviation for adelson velsky tree. it is a self balancing binary tree in which the difference of heights of left and right subtree cannot be more than 1.,2.5
-3631,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL Tree is a balanced Binary Tree.  Whenever a new node is added to an AVL tree it utilizes the left height and right height of that particular node on which the new node is added. \nif the difference between the LH and RH is > 1 than 4 rotation are possible Left Right Rotation , Right Left Rotation, Left Left Rotation and Right Right Rotation. All these steps an AVL Tree balanced.",2.5
-3632,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree in which maximum ,0.0
-3633,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),Avl tree is a tree that  uses rotation to self balance its height. ,2.5
-3634,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"avl is a weighted tree\nwhere weight is obtained and allowed weight is [-1,1] the tree is extended only in the direction where weight is maintained between -1 and 1\n",1.0
-3635,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),balanced binary tree in which each node is connected through balanced factor of subtracting or adding0,2.5
-3636,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is used to sort the binary tree with child nodes less than the value of parent node on the left and greater on the right ,0.0
-3637,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"Such a tree , in which the value of left is smaller than the parent node and the value of right child is bigger than the parent node is called AVL Tree.",0.0
-3638,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a special type of tree in which it maintains balance between the left and the right subtrees. if the balance is greator than 1 then it perform swap operation to maintain the balance between them.,2.5
-3639,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL is the balanced binary tree which have divided its tree may be in symmetry or it is equal divide in level. it is perform the operations like traverse, add or delete. by left to right and right to left.",2.5
-3640,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3641,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL Tree is a properly balanced binary tree where every node has 2 children except the leaf nodes and nodes are filled from left to right. To maintain the order rotations are also done in the tree.,2.5
-3642,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"avl tree are balanced  binary tree in which  we  balance tree by performing  certain rotation that are LL , RR  , LR , RL",2.5
-3643,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree :- It is Balanced Binary Tree. ,2.5
-3644,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a avalanche,0.0
-3645,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is  data structure that follows tree traversal or graph traversal algorithm,0.0
-3646,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL Tree is balancing tree in which Data is stored and then balanced by the rotations (LL, LR, RL, RR).",2.5
-3647,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"An  AVL Tree is a type of Tree in which we use rotations to add an element. rotations are LL,RR,LR,RL",1.0
-3648,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"in avl tree first element is considered as root node then each elements are arranged according by the comparison made by the parent node, element smaller than the parent shifted to left and element greater to parent shifted to right, and we have to check correct insertion of each element by checking its height from the ground and make necessary rotations like right right rotations etc",0.5
-3649,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree is a balanced binary search tree in which the difference of the weight of left and right side the tree can be -1,0,1. if the weight of the tree is not equal to the previously mentioned weights then we perform rotations in order to make weight = -1,0,1.",2.5
-3650,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),An AVL Tree is a modification  of the tree data structure such that the difference between the height of the left sub tree and right sub tree at each node is less than equal to one. This is done in order to ensure that the searching time for an element stays in O(logn) time and not become O(n) time in worst case.,2.0
-3651,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tress is a ,0.0
-3652,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is balanced tree in which right side is greater than parent and left is smaller than parent and if goes unbalanced then rotations are performed  ,2.5
-3653,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),,0.0
-3654,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),AVL tree is a tree in which height of tree is maintained there are only 2 maximum child nodes which are distributed left and right.,0.0
-3655,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl is a part of binary tree in which time complexity is low ,0.0
-3656,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),"AVL tree follows property of a balance binary tree, with its all nodes arranged properly from left to right in a minimum or a maximum order.",2.5
-3657,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is maintain height of the tree  and distribute element left or right,2.5
-3658,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),self balanced binary search tree.,2.5
-3659,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),it is a self balancing binary search tree in which different rotations depending the different conditions \nthe following rotations can be done in a avl tree -\nrr\nll\nrl\nlr,2.5
-3660,What is an AVL Tree?,AVL trees are height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor. BalanceFactor = height(left-sutree) − height(right-sutree),avl tree is also known as  avalanche tree and used in sorting.,0.0
-3661,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3662,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if there are total 6 edges in a graph then 6-1=5  spanning trees  are possible i.e. vertices-1  are the total spanning trees possible,1.0
-3663,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have if we have v vertices the number of spanning trees can be v-1\n,1.0
-3664,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",minimum: 1\nmaximum: No of edges - 1,1.0
-3665,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Let V be the number of vertices inside a graph and E be the edges. Then maximum number of spanning trees is |E|-1 and  minimum number will 1.,1.0
-3666,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if there are V no. of vertices then there will be V-1 spanning tree,1.0
-3667,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3668,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",No of spanning tree can be calculated by the formula  |E|^C subscript |V-1|,1.0
-3669,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",|Edges| C |Vertices -1|  - no of cycles\n\n{ edges! \\ [ ( edges-vertices-1 )! * (vertices-1)! ]} - no of cycles  ,1.0
-3670,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",with n vertices\nspanning trees = n(n-2),1.0
-3671,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can has maximum of n of trees where n is the total no. of nodes in that tree.,1.0
-3672,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",It has many spanning tress and follows the formula :- [2n C ( v+ e ) ] -[ no. of cycles] .,1.0
-3673,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","The spanning tree can have (E - 1) edges and all vertexes  from the graph , (2^n-1) spanning tree can be form.",1.0
-3674,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Spanning tree in a graph is when you remove all the extra edges  forming a cycle in a graph. A graph can have multiple spanning trees.,1.0
-3675,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have infinite number number of spanning trees.,1.0
-3676,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",There can be multiple spanning tree provided cost of each tree is minimum.,1.0
-3677,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The maximum number of spanning trees in a graph is equal to the number of children of the root node,1.0
-3678,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3679,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have minimum spanning trees equals to its (vertex-1),1.0
-3680,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","if a graph has V vertices and E edges, the number of spanning trees it can have is (Ec(V)) - 1.",1.0
-3681,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have many spanning tree if no cycles are formed in any of them. There can be only one or no more than a total of \,1.0
-3682,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have many  spAnning trees till the moment there are no cycles formed . also there can be only be minimum spanning tree of a ,1.0
-3683,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","The spanning trees depend upon the no of cycles present in the graph. it could have n-1 spanning trees.,",1.0
-3684,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","AVL tree is a special type of tree which stores values in such a way that element having value less than root node is stored in its left child, and element greater than data in root node is stored in right child. ",1.0
-3685,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have minimum spanning tree equal to number of edges.,1.0
-3686,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",No. of edges-1,1.0
-3687,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","E(E-1)/2, where E is the number of edges.",1.0
-3688,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","E(E-1)/2,\nwhere E is the no. of edges.",1.0
-3689,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have same no of spanning tree as its edge count is.,1.0
-3690,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have 2^n spanning trees where n is the number of nodes.,1.0
-3691,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",number of spanning trees that a graph can have is equal to the number of edges that a graph has,1.0
-3692,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2 to the power of number of vertex.,1.0
-3693,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if a graph has n edges it can have n-1 spanning trees where each tree has same no. of vertices.,1.0
-3694,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Total number of spanning trees is equal to number of edges the graph has,1.0
-3695,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can has Many spanning trees depending upon the tree structure. But the graph has only one minimum spanning tree.  ,1.0
-3696,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph has n^(n-2) spanning trees. Where n represents number of nodes.,1.0
-3697,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n^(n-2) spanning trees a graph can have.,1.0
-3698,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have spanning trees equal to its vertices.\n,1.0
-3699,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A tree traversal means displaying all the elements of a tree one by one in a particular order. We can traverse tree in 4 order:\n1. pre-order.\n2. in-order.\n3. post-order.\n4. level-order.,1.0
-3700,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have multiple spanning trees but the maximum spanning trees can be equal to the number of vertices.,1.0
-3701,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","A graph can have multiple spanning trees, but can only have Minimum Spanning Trees equal to the number of vertices in it.",1.0
-3702,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","for n edges in the graph, there can be n factorial spanning trees.",1.0
-3703,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have multiple spanning tree. The one with the less no of participants is called minimum spanning tree.,1.0
-3704,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","In AVL tree the root node is the largest, with exactly to children>\nwhere the left child<right child",1.0
-3705,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have many spanning trees .It depends on the number of  vertices of the graph....\nA graph with 'n' vertices can have 'n-1' spanning trees.,1.0
-3706,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph having E number of edges can have E-1 number of spanning trees.,1.0
-3707,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph has maximum |V| - 1 spanning trees where V is number of edges.,1.0
-3708,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",|V| - 1 is the minimum number of spanning tree a graph has. ,1.0
-3709,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",M-1 (M= number of vertices in the graph).,1.0
-3710,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",M-1 where M is the number of vertices in the graph.,1.0
-3711,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Graph contains V number of vertices and E number of edges so the spanning tree will be having V number of vertices but E-1 number of edges. It can have 2^V or 2^N number of spanning trees.,1.0
-3712,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Number of spanning trees are equal to number of edges in a graph.,1.0
-3713,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","for n nodes , n-2",1.0
-3714,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have no more than n^n-2 where for a graph with n edges.,1.0
-3715,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",tree can has 2n spanning trees where n is the height of tree,1.0
-3716,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3717,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",there are n-1 spanning trees whereby we can find minimum spanning trees via prims or Kruskal algorithm.,1.0
-3718,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have spanning trees equal to the no. of vertices. ,1.0
-3719,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Maximum number of mst a graph can have V ( where V is the number of vertices in the graph,1.0
-3720,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Infinite,1.0
-3721,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^n,1.0
-3722,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",minimum n^(n-2),1.0
-3723,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^n,1.0
-3724,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",there can be multiple spanning trees for a single graph,1.0
-3725,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have infinite spanning trees at max.,1.0
-3726,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Graph can have many MSTs n-1,1.0
-3727,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have n-1(where n is no. of edges)  spanning trees.,1.0
-3728,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^(N-1) N IS NO OF VERTEX,2.5
-3729,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n^(n-2),1.0
-3730,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","A graph can have multiple spanning trees depending on how many edges it has between its nodes. If only a single edge exists between any two adjacent nodes in a graph, it will have only one spanning tree. However, if it has multiple edges then we can have multiple spanning trees. If there are weighted edges, the spanning tree with the minimum weight is called a minimum spanning tree. ",1.0
-3731,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,1.0
-3732,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n^(n-2),1.0
-3733,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph having vertex v and edges e can have ,1.0
-3734,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","if a graph has V no. of edges in its structure, then it will have (V-1) no. of spanning trees possible depending on the different weights of the graph and paths of traversal.",1.0
-3735,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",1 to many.,1.0
-3736,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",edge - 1;,1.0
-3737,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",the graph can have n-1 no. of spanning trees where n is the no. of nodes in the graph.,1.0
-3738,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",the number of spanning trees of a graph are equal to the number of vertices of the graph,1.0
-3739,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The graph can have ,1.0
-3740,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",no of nodes-1,1.0
-3741,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",the spanning tree an graph can have n^n-2,1.0
-3742,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","If there are N vertices, a graph can have N - 1 Spanning Trees",1.0
-3743,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have as many spanning trees as possible and the solution might differ so there is no one correct answer.But there can be only 1 Minimum spanning tree unless the weights are similar. ,1.0
-3744,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can has a maximum of n^(n-2) number of spanning trees. ,1.0
-3745,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",as many no of edges are there in a graph,1.0
-3746,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",(number of vertices -1)  is the number of spanning trees a graph can has,1.0
-3747,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have minimum spanning tree= number of number of edges-number of vertices+1 ,1.0
-3748,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",(n^(n-2)) spanning tress can be made from a graph. ,1.0
-3749,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",spanning trees can a graph has depend on number of vertices. if graph has 6 vertices then graph have 6 spanning trees.,1.0
-3750,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n^n-2 number of spanning trees where n is number of nodes.,1.0
-3751,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have many spanning trees unless it is not forming cycle.,1.0
-3752,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Minimum 2,1.0
-3753,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The no. of spanning tree are equal to the no. of vertices that are making a round.,1.0
-3754,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.", minimum of  1 and maximum n^(n-2)\n,1.0
-3755,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",max spanning trees can be no. of vertex+1,1.0
-3756,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The number of spanning trees a graph can have is the number of nodes it has.,1.0
-3757,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n (n-2),1.0
-3758,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",AVL Tree is the Binary Search Tree with the condition that the Nodes have weights. It is a balanced Binary tree.,1.0
-3759,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",AVL tree is a balanced binary tree,1.0
-3760,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have as many/multiple spanning trees as there are there are the number of ways of travelling through it.,1.0
-3761,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",number of vertices of graph - 2,2.0
-3762,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",V-2\nV=no of vertices,1.0
-3763,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",equal to number of edges.,2.0
-3764,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n to the power (n-2).,1.0
-3765,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^n,2.0
-3766,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",maximum nn-2,1.0
-3767,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Graph with number of edges as E can have E number of spanning trees..,1.0
-3768,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have (V-1) spanning trees.,2.0
-3769,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",graph can have multiple spanning trees.,2.0
-3770,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",many but only one mst,1.0
-3771,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Number of spanning trees= number of nodes + number of edges to be removed to make a spanning tree,2.0
-3772,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",number of spanning trees that  a graph has,2.0
-3773,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","(Number of Edges) Combinations (Number of vertices - 1) [We have to ensure parallel/self edges are removed and while formation of MST, cycles are not formed]",1.0
-3774,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Avl tress are red black tress.,2.0
-3775,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",equal to the no of vertices,2.0
-3776,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",there are N^(N-2)   spanning trees in the graph where N are number of nodes.,1.0
-3777,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n^n-2,1.0
-3778,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","An additional balanced binary search tree is the AVL tree. Similar to red-black trees, they are not completely balanced, but pairs of sub-trees have height differences between them of not more than 1, preserving an O(logn) search time.",1.0
-3779,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",(V*E)/2,1.0
-3780,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","n(n-2), where n is no. of vertices",2.0
-3781,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Spanning trees can be made by removing edges from the original tree by keeping in mind that the resulting graph should not be connected. ,1.0
-3782,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have many  spanning trees as possible but only one minimum spanning tree.,1.0
-3783,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",spanning tree = vertex -1,2.0
-3784,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A connected graph has 2^n spanning trees where n is number of nodes.,2.0
-3785,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","quick sort select a random allie and then according to that it sorted the present array, then this process is applied to further sub arrays until we get the sorted array.",1.0
-3786,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Only three spanning trees are possible of a complete graph.,2.0
-3787,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","E - V + 1 where E is the no. of edges , V is the no. of vertices in the graph.",2.0
-3788,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have many number of spanning trees depending on number of nodes and which node is connected to which one,1.0
-3789,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",infinite,1.0
-3790,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The number of edges.,1.0
-3791,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",An AVL tree is a special type of Binary search Tree in which the difference between the left and right height of the subtrees cannot exceed 1.,1.0
-3792,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",maximum of  1.3 trillion or 3^20 ,2.0
-3793,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have infinite number of spanning trees.,1.0
-3794,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n-2 graph\n,1.0
-3795,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",not more than [n*n-1] spanning trees can a graph has,2.0
-3796,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can has E number of spanning trees where E is the number of edges of the graph.,2.0
-3797,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",it can have (n-1) spanning where n is number of nodes,2.0
-3798,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have infinite spanning tree,2.0
-3799,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have N+1 spanning trees if number of nodes is N.,1.0
-3800,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have number of vertices - 1 spanning trees,1.0
-3801,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",avl tree is similar to red black tree but in this case the height difference of the left and right side is maintained thus it is used to make a tree more balanced.,1.0
-3802,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph has same number of spanning trees as its number of edges.,1.0
-3803,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",at most N where N is the number of node in graph ,1.0
-3804,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,
-3805,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","A spanning tree is  a sub-graph of an undirected connected graph,which includes all the vertices of a graph with a minimum possible number of edges.,if vertex is missed ,then its not a spanning tree. The edges may or may not have weights attached to them.",2.0
-3806,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have multiple spanning trees and it depends of which graph we are creating the spanning tree.,2.0
-3807,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have many spanning trees,2.0
-3808,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","AVL Tree refers to the tree that is created by inserting each and every element and then by changing the position ,if required.",1.0
-3809,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",total edges -(n-1) where n is the number of nodes ,2.0
-3810,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have spanning trees equal to number of nodes in that graph.,2.0
-3811,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can has many spanning trees but it has only one minimum spanning tree.,2.0
-3812,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","a graph can hav any number of spanning tree. It would mainly depend on the number of edges, more the edges, more spanning trees can be formed.",2.0
-3813,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if the number of vertices is 1 then the graph can have v-1 spanning trees.,2.0
-3814,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have mutiple spanning tree we can find by removing edges and hence having no. of edges! possibilities.,2.0
-3815,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",no. of spanning trees is equal to no. of edges,2.0
-3816,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3817,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","there are many spanning trees which are there for a graph, but there can be only one spanning tree of the graph.",2.0
-3818,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Condition the number of spanning tree= the (n-1) edges and 'n' number of vertices.  ,2.0
-3819,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",An AVL tree is a type of BST in which the parent node is bigger than the child node and it is made by rotations.,2.0
-3820,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have many spanning trees till it follows the condition of v vertices and  v-1 edges.,2.0
-3821,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if number of vertices is N the there will be N-1 spanning trees,2.0
-3822,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have spanning trees equal to (V-1) !,2.0
-3823,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have 2^(n - 1) spanning trees where n is the number of vertices.,2.0
-3824,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have E (edges of graph) number of spanning tree.,2.0
-3825,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^(n-1) spanning trees can be made. ,2.0
-3826,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The total number of spanning trees with n vertices that can be created from a complete graph is equal to n (n-2). ,2.0
-3827,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n-1 edges spanning tree a graph can have,2.0
-3828,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","the number of spanning trees that a graph can have depends on the number of nodes present in the graph.\nfor N nodes , there could be 2*N spanning trees possible",2.0
-3829,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph with e edges and v vertices can have (e-1) number of spanning trees,2.0
-3830,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",1,0.0
-3831,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",no of spanning tree a graph can have is    N^(N-2),1.0
-3832,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",we can use maximum (n^n-1) spanning trees in a graph,1.0
-3833,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A complete unidirected graph can have maximum of (n^(n-2)) number of spanning trees.,1.0
-3834,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",\nmaximum spanning trees in a graph can be is n^n-2 in which n is the number of nodes in the graph,1.0
-3835,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have a maximum of n^(n-2) spanning trees where n=number of nodes ,1.0
-3836,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n^(n-2),1.0
-3837,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",THE NUMBER OF SPANNING TREE = V*E,1.0
-3838,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^n,1.0
-3839,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have a minimum of n^(n-2) spanning trees.,1.0
-3840,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",It is not necessary that a graph can have only one spanning tree it can have many spanning tree also there can be more than one minimum spanning tree (MST).,1.0
-3841,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have many spanning trees in which one them is minimum spanning tree which has minimum weight from source to destination.,1.0
-3842,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have many spanning trees.,1.0
-3843,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph has n^(n-2) spanning trees.,1.0
-3844,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have any number of spanning tree given the sum of the weight of all the edges combined in them is minimum possible and no cycle present\n,1.0
-3845,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",suppose a graph having e edges involved in cycle then e no spanning trees are possible,1.0
-3846,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a complete undirected graph can have a maximum n^(n-2) number of spanning tree,1.0
-3847,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","Tree traversal is basically our pointer which traverses through a data structure which is in tree form, It can be done through various methods. And it includes traversing through different nodes from root nodes to leaf nodes to find/manipulate our elements. For ex- BFS,DFS, etc.",1.0
-3848,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.", It can have a  maximum of  (n^n)-2.,1.0
-3849,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",If there are n edges.\nn!,1.0
-3850,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","A graph can have a maximum (n-1) spanning trees, where n = no. of vertices graph have.",1.0
-3851,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have multiple spanning trees it depends on number of edges and vertices.,1.0
-3852,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","If n is the no of vertices,then a graph can have n-1 spanning trees.",1.0
-3853,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",We can have different number of spanning trees in a graph depending upon the number of edges and vertices present in the graph.,1.0
-3854,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",There can be multiple spanning trees. If there are 0 pairs of edges with the same weight then the graph will produce an identical minimum spanning tree.\nA general rule is that ANY GRAPH HAS N-1 SPANNING TREES. \nwhere N = number of vertices or nodes in the graph.,1.0
-3855,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",there can be 1 or more than 1 spanning trees in a graph,1.0
-3856,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",many spanning trees can be there depending upon the edges and vertices,1.0
-3857,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","tree traversal means visiting every node from the parent to the root nodes. Inorder , Preorder, postorder and levelorder are the various ways to traverse a tree.\n",1.0
-3858,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have 1 or more than 1 spanning tree. spanning tree is a tree in which all the nodes are covered and there is no cycle present.,1.0
-3859,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",number of edges(number of edges -1),1.0
-3860,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have number   n*(n-1)  of spanning trees.,1.0
-3861,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2,0.0
-3862,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^n,0.0
-3863,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",only one and depends on the graph path costing,0.5
-3864,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n^2 (n being number of vertices),0.0
-3865,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have indefinite number of spanning tree.,0.0
-3866,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",number of spanning trees a complete graph with n nodes can have is : 2^n,0.0
-3867,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3868,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",there can be as many spanning tree as we want but minimum spanning tree (total no.of vertices-total no.of edges) ,1.0
-3869,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","if a graph has m trees than the no, of spanning tree are m-1",0.5
-3870,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",There can be infinite spanning trees for a given graph.,0.0
-3871,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The number of spanning trees that a graph can have is equal to the number of edges in the graph.,1.0
-3872,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",infinite,0.0
-3873,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",e-1 where e is the number of edged,0.0
-3874,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The number of spanning tree that a graph can have is equal to the n*(n-2) where n is the number of vertices that it contains. It also contains a minimum spanning tree that can only be one and unique for a graph.,0.0
-3875,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Spanning Tree is not unique to a graph. Actually a graph can have a number of spanning trees.,1.0
-3876,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",infinite spaning tree can a graph has,0.0
-3877,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","it can have 0 (it its not connected), 1 or more than 1 as well.",1.0
-3878,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",no of subgraphs -1    or n(n-2)    ,0.0
-3879,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3880,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if a graph has n vertices then there will be \nn spanning trees for closed graph and n-1 for unclosed graph ,0.0
-3881,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",,0.0
-3882,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","let a graph G has V vertices and E edges, represented as G(V,E) ,then a graph can have maximum V number of spanning trees.",0.0
-3883,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",It depends on the conditions that how many graphs are to be formed.,1.0
-3884,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",graph can't have a spanning tree because in graph cycle can be formed but in spanning tree a cycle can't be formed so no one,0.0
-3885,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have several spanning trees depending on what path you choose for traversal.,1.0
-3886,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if tree has n edges then we can have n number of spanning trees,0.0
-3887,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",if graph has n edges then n number of spanning tree\nMinimum weights  can a graph has.,0.0
-3888,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2,0.0
-3889,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",infinite ,0.0
-3890,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",n= no. of edges\n-> No. of spanning trees = n,0.0
-3891,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",a graph can have multiple spanning  trees but only one mst.,1.0
-3892,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",spanning trees can have  acco,0.0
-3893,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Spanning trees a graph can have are :- 2^(n)\nwhere n = no. of edges in a graph,0.0
-3894,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",A graph can have multiple spanning trees,1.0
-3895,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",2^n - no of closed path ,0.0
-3896,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",no.of nodes -1,0.0
-3897,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","in graph G , it has V vertices and E edges , so it can make V number of spanning trees",0.0
-3898,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",number of edges - 1,0.0
-3899,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",The no of spanning tree is depends on the no of vertex in the tree we can have the equal amount of spanning tree the no which we have in a graph ,0.0
-3900,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.","A graph can have many spanning trees, a generalized formula fo the total no of spanning trees a graph can have 2^(N-1), where N is no of edges.",0.0
-3901,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",one,0.0
-3902,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",Minimum spanning tree (Mst).,0.0
-3903,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",the number of spanning trees possiable is 2^n,0.0
-3904,How many spanning trees can a graph has?,"It depends on how connected the graph is. A complete undirected graph can have maximum nn-1 number of spanning trees, where n is number of nodes.",there would be no spanning tree in a graph,0.0
-3905,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-3906,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap stores the values dynamically i.e at the runtime ,2.0
-3907,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",in a heap data structure the elements can be represented from either max to min or min to max depending on the heap we use if we use min heap the minimum element is present in the root node if we use max heap the maximum element is present in the root node heap can be used as priority queue ,2.0
-3908,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap Data Structure has two types: minheap and maxheap. Heap arranges data in a state tree and then checks if any child is not smaller(minheap) or larger(maxheap)than its parent,2.0
-3909,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure that stores data in min or max form. min heap has lowest data as root and each element follows this. Vice versa in maxheap where max is at root and else are smaller and same logic is applied to each node.,2.0
-3910,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a data structure used to store data in tree type format and using build heap and always parents value is greater than its child,2.5
-3911,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a tree based data structure in which all nodes of tree  are in specific order .,2.5
-3912,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-3913,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is implemented using tree \nthere are two types of heap \nmin heap - parent node is always less than its child nodes\nmax heap - parent node is always greater than its child nodes,2.0
-3914,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly,1.0
-3915,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a non linear  data structure which uses dynamic memory and whose size can be increased or decreased as per our choice and which stores the index of the value present in array.,2.5
-3916,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is way of colleting the data one over another for the usage . We can store the data in two forms of the heap , that is min heap or max heap . \nIn min heap the data is stored in ascending order and descending order in max heap .",2.5
-3917,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap in data structures is a function  which consist of two form one min heap and max  heap. In min heap all the values are arranged which are minimum and then are read or store. And in max heap all the values which are large are store and read.,2.0
-3918,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a data structure similar to that of a tree but stores the data in two different kind of formats , either by using MAXIMUM values or  MINIMUM values.",2.5
-3919,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-3920,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A tree where parent node is greater/smaller than the all children node. ,2.5
-3921,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",The heap data structure employs a condition where parent is either smaller or greater than all its children elements,2.5
-3922,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A graph can have, n^(n-2) number of spanning trees.",0.0
-3923,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap  data structure is used to sort the array in minimum complexity of 0(logn) it gives the minimum or maximum element on the top according to priority queue used ,2.0
-3924,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure is a data structure which is used to extract the max element(max heap) and min element(min heap) easily.,1.0
-3925,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.", spanning trees for a graph with  \,2.5
-3926,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap data structure is very similar to the tree data swtructure , it has a root and it has nodes . ",1.0
-3927,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure stores value in the form of tree and then the heap is sorted where increasing or decreasing order (max or min heap) and can be useful for traversing the particular element. ,0.0
-3928,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Spanning tree of a graph is a special tree where number of vertices are same as parent graph, but there should not be any cycles present. ",0.0
-3929,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is data structure , like priority queue .\nwe have here max and min heap to solve our problem like max and min operation  in an efficient way ",2.5
-3930,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Two types of heap Min-heap and Max-heap.\nIn min heap the data is sorted in ascending order in the form of a tree in which the minimum element is the root .\nIn max heap the data is sorted in descending order in the form of a tree in which the maximum element is the root .,2.5
-3931,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",It is a complete binary tree where every node has either 0 or 2 children. There are two types of heaps - minHeap where the minimum element is placed at the root and maxHeap where the maximum element is placed at the root.,2.5
-3932,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure is a complete binary tree which has either 0 or 2 children and further heap is divided into two sub categories minheap and maxheap. Minheap has minimum element in the root node and maxheap has maximum element in the root node. The traversal goes down by comparing both the children and the one min is swapped with the parent node for that child.,2.5
-3933,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",if is the data structure used to store information in sorted order. If the Heap is Max heap  then it will store data in such a manner that data of the child node is always less then the parent node. If it is min heap then it will store in such a manner that data of parent is always less then child. ,1.0
-3934,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a data structure that stores data in the form of a tree either in ascending order(min-heap) or descending order(max-heap).,2.0
-3935,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is used when we have to store the elements which are already sorted. There are two types of heaps that is min heap and max heap. max heap is a heap in which the child node is always less than the parent node, whereas in min heap it makes sure that the parent node is less than the child node.",1.0
-3936,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is basically of two types:\n1)  min heap: value of parent node is lesser then the its children\n2) max heap: value of parent node is greater then the its children,2.0
-3937,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure which is basically a complete binary tree in which each node will be greater than its child nodes or smaller than its child nodes.,2.0
-3938,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is the data structure with time complexity of n log(n).\nInstead of sorting nodes one by one before placing in the tree. Heap helps us to sort them in the tree only there are 2 types of heap max heap and min heap.\nit select the minimum value in the heap and traverse it upwards by swapping from upper node one by one and then arrange if there is disturbance in other nodes this is known as min heap.,2.0
-3939,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-3940,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a data structure based on tree. A heap is a complete binary tree.,1.0
-3941,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure that is made up of complete binary tree. It helps in extracting max or min element.,2.0
-3942,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a kind of data structure which help in arranging the data in a ascending or descending order . Heaps are of 2 types MIN-HEAP and MAX-HEAPS.,2.0
-3943,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","An AVL tree is the one which is a balanced binary tree i.e. every node has its (max left child length- max right child length)=1,0,-1.",0.0
-3944,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a complete binary tree in which we cant move to the next level without filling the current level. It can be made using priority queue.,2.5
-3945,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a data structure, which all the elements of a tree are stored in an array or linked list. If the parent node is at nth index in the array, then the first child is at 2n+1, second child at 2n+2 and so on. It is a representation of a tree in form of an array or linked list.",1.0
-3946,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap refers to the data structures where the root and intermediate nodes have right and left children which are 2n+1 and 2n+2 respectively for parent node located at n index. Heap can be implemented using arrays, linked list and trees. In case of max and min heap , parent node is greater or smaller than child nodes respectively and on any deletion or insertion of a value it rearranges to maintain the same conditions..",2.0
-3947,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a data structure that is based on the advanced version of queues that is priority queues. ,1.0
-3948,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A graph with n vertices can have n-1 spanning trees. As there can be multiple ways to join all vertices but not create a cycle.,0.0
-3949,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is another type of array of a data structure.,1.0
-3950,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap in data structure uses trees for implementation, there are two types of heaps a max heap where all the child nodes are smaller than the parent element and min heap where all the child nodes are greater than the parent node. ",2.0
-3951,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is Data Structure is a dynamic data structure that is used to store data either in ascending order(Min Heap) or Descending Order as Max Heap.,2.5
-3952,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a type of data structure to store the data ascendingly or in descending order i.e. 'min Heap' or 'max Heap'. ,1.0
-3953,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a kind of data structure to store the data ascending or in descending order.,1.0
-3954,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is used to store the data in increasing or non-increasing order.,1.0
-3955,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a data structure that stores data which have operations like heap sort and heapify. These are of two types: Min heap and Mex heap. Min heaps are formed by keeping the root node minimum and as data at the leaf as maximum whereas in case of max heap we have root as maximum and leaf as minimum.,2.5
-3956,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A data structure (array) which can be represented as the complete binary tree and having it parent node smaller than its children node(min heap) is called a heap.,2.5
-3957,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a specialized tree-based data structure that is used to maintain a collection of elements where the highest (or lowest) priority element can be accessed quickly. It has two main variants: max-heap and min-heap. In a max-heap, the parent node is always greater than or equal to its children nodes, while in a min-heap, the parent node is always less than or equal to its children nodes. Heaps are commonly used in algorithms such as heap sort and priority queues.",2.5
-3958,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is an data structure in which the tree is a complete binary tree and is a special binary tree.,2.0
-3959,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a dynamic data structure that contains the address of values rather that storing them itself which makes it fast and efficient,1.0
-3960,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",It is the memory in which data is stored temporarily when operations are being carried out.,1.0
-3961,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap is an optimized form of priority queue which is used to store the numbers in such a manner that it is easier to find the number and do the required operations,2.0
-3962,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure is basically a binary tree. It can be of two types - min heap and max heap\nHeap in data structure is a binary tree.,2.5
-3963,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a data structure in which the parent node is lesser or greater than the children node\nMin heap has parent lesser than the children node\nMax heap has parent greater than the children node\nHeap is used for heap sort. Heap also has a lot of function like extract min, decrease key etc.",2.5
-3964,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is type of tree which is arranged in increasing or decreasing order in which leaf value is less than right value.,1.0
-3965,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",it is a binary tree in which root is compared with children it is of 2 types - max heap & min heap,2.0
-3966,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a tree based data structure in which the tree is completely binary tree,2.0
-3967,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly.\n\n,2.0
-3968,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap is a type of data structure that stores the data similar to a tree and is usually when we need to remove the root node priority (highest or lowest) wise. It is also used to insert values which will be stored either in min heap ( lower value priority) or max heap (higher value priority),2.0
-3969,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is used to store in such a manner that either the minimum data stays on top and then bigger than it becomes its child and then again the child of it follows that its child are bigger than it or  the maximum data stays on top and then its child are smaller than it and again there child are smaller then them.,2.5
-3970,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure like stack and queue that stores values. Binary heap can store value accordingly and is a preferable method in many cases for searching and sorting \n,2.0
-3971,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a priority queue where we can decide the order of insertion and deletion based on our requirements.,2.0
-3972,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a specialized tree-based data structure which is essentially an almost complete binary tree that satisfies the heap property. Their are two types of max heap and min heap .,2.0
-3973,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",it a tree in which the tree is complete binary tree.it implements priority queue.in this tree the highest or the lowest element is thee root node.,1.0
-3974,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a data structure where the data is stored in the form of a sorted tree where the parent node can have multiple child nodes. A heap can either be a min heap or a max heap depending on the way it is sorted.,2.0
-3975,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heaps are tree like structures where the child element is always greater or smaller than the parent element. Examples are min heap, max heap, etc..",2.5
-3976,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",it is tree based data structure in which it is a complete binary tree. if the most effecient implementation of the data type priority queue,1.0
-3977,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",\nThere are three types of heaps\n1.Binomial heap\n2.Fibonacci heap\n3. Min heap/ max heap,1.0
-3978,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","a heap is a data structure used to arrange data in a sorted manner, we take the first data inputted as head of the heap and then keep on inserting data below it, data is then shifted and pushed down accordingly considering whether it is min heap or max heap.",2.0
-3979,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-3980,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is nothing but a special tree whose root node has a lesser left child and greater right child.,2.0
-3981,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a data structure which uses a tree to arrange the values given in an ascending or descending order.,2.5
-3982,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap data structure is implemented by following the heap property when connecting the two nodes of the heap\nHeap property is when the root element is greater or smaller than as mentioned than the children nodes of the heap,2.0
-3983,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly.,2.0
-3984,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",it is a kind of tree,0.0
-3985,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is the data structure of arrangement which gives the max or min element  from the set of element ,1.0
-3986,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a form of a tree, there are two types of heap, min heap and max heap\nIn a min heap, the parent node is smaller than that of its child nodes\nIn a max heap, the parent node is greater than that of its child notes",2.5
-3987,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap is a tree only with few constraints ,2.5
-3988,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a special case of balanced binary tress data structure where the root node key is compared with its children and arranged accordingly.,2.0
-3989,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is data structure which has the maximum or minimum element at the root node,2.0
-3990,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a kind of tree which has the root node as either max element or min element .It can be of 2 types min heap(root=min) or max heap(root=max),2.5
-3991,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","a heap stores data according to a constraint in a tree  in the form of binary heap,binomial heap,max heap,min heap fibonacci heap depending upon the operations neede to be performed.",2.0
-3992,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is the balanced binary tree, which includes min heap and max heap.",1.0
-3993,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a special tree based data structure in which the tree is complete binary tree.,0.0
-3994,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a tree based data structure in which all nodes of a tree are in specific order.,1.0
-3995,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap is a data structure used to store and sort data by assigning them to left and right child of the root node and so on. ,2.5
-3996,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a linear data structure which is implemented by queue,2.0
-3997,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a data structure that store a data in encoded form , if we want retrieve out we have to decode it by the type of heap we used to encode it.",1.0
-3998,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a complete binary tree is heap,1.0
-3999,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",it is a efficient data structure that helps it has max heap and min heap it has max/min element at top\nit helps to find kth smallest or largest element ,2.0
-4000,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a data structure which can be implemented using priority queues. A heap tree is a tree in which on deletion, the highest element (in case of maxHeap) or the lowest element (in case of minHeap) gets deleted first.\n",2.5
-4001,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap in data structure is arrangement of data by insertion of max elements first or by min elements an order of max elements first is maintained\nmax elements arranged are called max heap whereas elements arranged accord,2.5
-4002,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A graph has (|E|-1-no. of cycles) number of spanning trees spanning trees.,1.0
-4003,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",n^(n-2) where n has no. of nodes,1.0
-4004,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap data structure is a tree data structure and is in the form of min/max heap. In min heap the parent node is greater than both of its children and the element to be added is added to the last node.,2.5
-4005,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap gives us the largest or the smallest value that we want in any case\nlike max heap, min heap.\ndone using priority_queue\nin case of max heap we get maximum element at the top\nand in case of min heap we get the minimum element at the top.",1.0
-4006,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A Heap data Structure is type of Data Structure in which we used to store data in pilled up manner \nex. priority queue ,1.0
-4007,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,
-4008,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap in a special tree based data structure in which the tree is a complete binary tree where all nodes of a tree are in a specific order.,2.0
-4009,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",data structure in which maximum and minimum values stores at the top of the tree it vcan be of 2 types max an min heap. max heap have maximu value on to. while min heap have mim value on top.,1.0
-4010,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a tree-based data structure in which all the nodes of the tree are in a specific order. ,1.0
-4011,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap refer to a large memory block. Heap is based upon LIFO- Last in first out principle. ,2.0
-4012,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a data structure to store elements in form of a tree. There are two types of heaps\n1) Min heap- the root node will contain the minimum value element\n2) Max heap - root node will hold the maximum value element.,1.0
-4013,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is binary tree. it has two types- max and min heap. in max heap, the maximum element becomes the root node and in min heap, minimum element becomes the root node.",1.0
-4014,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a binary tree that arranges data in order( ascending - min heap, descending - max  heap)",2.0
-4015,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap data structure is basically a data structure in which the data is stored in the form of :\nMin Heap: every child node is greater than the parent node. (root node is minimum)\nMax Heap: every child is lesser than the parent node. (root is maximum).\nPriority Queue: The elements are retrieved according to the specified priority. (minimum or maximum, lesser then or greater than).",1.0
-4016,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",in this data structure the value of the parent node must be greater than or equal to (in case of max heap ) or less than or equal to (in case of minheap) to its child nodes.\n,2.0
-4017,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is like a tree which stores data in an ordered manner. Min heap accounting for ascending as the head node is the smallest value among them all and Max heap accounting for descending as it places the largest value as the head node. ,1.0
-4018,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",at maximum the graph can have (no of edges+1) spanning tress.,2.0
-4019,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure stores values such that greater(maxheap) or lower(minheap) value is parent of lesser or greater values. Used to extract minimum of the values list.,2.0
-4020,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is type of data structure where the first element is  either smallest or largest depending upon the heap and the parents of each element lie on 2N and 2N+1.,1.0
-4021,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,
-4022,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",maximum number of spanning trees : n^(n-2),1.0
-4023,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a tree data structure where the elements are structured in a way such that the highest value element is on the top (max-heap) | or the minimum value element is on the top (min-heap). Heaps are used to implement priority queues.,2.0
-4024,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a complete binary tree, and the binary tree is a tree in which the node can have utmost two children. ",2.0
-4025,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is like a tree data structure but it does not store elements in random order. It has two types - Min Heap and Max Heap. Mean heap stores the elements in increasing order and Max Heap stores in decreasing order.,2.0
-4026,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",there are two types of heap that are  min heap and max heap. Min heap is a tree in which the node will have the minimum element.,1.0
-4027,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",It is similar to tree\ndata stored in ordered manner\nmin heap -> smallest e;lement in head node,2.0
-4028,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a data structure which contains either the maximum(max heap) or minimum (min heap) value on the top of the tree.,1.0
-4029,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","tree traversal is a way of traversing the binary trees in which we cover all the elements, It is of 3 types -> preorder, inorder, postorder",2.0
-4030,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a special case of balanced binary data structure where the root node key is compared with its children and arranged accordingly.,1.0
-4031,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heaps are the non linear data structures which stores the data hierarchially in form of trees. There are nodes and each node  ,1.0
-4032,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a tree data structure in which value stored at each node is smaller than its children in case of min-heap or value stored at each node is greater than its children in case of max-heap.,1.0
-4033,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap data structure create two heap  max heap or min heap .In min heap top element will be minimum and last element will be maximum and  max heap will be opposite of it .,2.0
-4034,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a data structure in which the elements are placed in such a manner that all the elements before the root are smaller than the root and all the elements after the root are larger than the root. It is a flexible d.s, with applications :-\n1)Fibonacci heap\n2)Binary heap\n3)Heap sort",1.0
-4035,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",2 * Number of Edges,2.0
-4036,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap data structure is a process by which you can optimise the solution to less time complexity by using heap , in this we make heap of small sizes and then we perform the work , lie min heap concept is used in kruskal's algorithm for reducing the time of kruskal;s algo from N^2 to (nlogn)",2.0
-4037,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","a heap is a DATA structure which is a sorted tree and the min\\max element can be prioritised as the top node, \nthere are several types, binomial heap, min heap max heap etc",2.0
-4038,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a special balanced  binary  tree where root node ,2.0
-4039,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a type of data structure that helps us into the efficiency of the program and can use global variable in it,1.0
-4040,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a binary tree which follows a certain heap property. Like a max-heap will follow max-heap property which means the in a max heap the topmost element of the heap will be always the largest element in the heap, whereas in  a min heap the topmost element of the heap will be always the smallest element in the heap.",1.0
-4041,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is of two type min and max which allow us to make min heap or ma heap,2.0
-4042,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a data structure that is used to store information in tree like formation in minimum or maximum manner which is performed by heapify function\nTwo types of heap\na)Min heap\nb)Max heap,1.0
-4043,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a data structure in which topmost element is minimum and its children are bigger than the node and so on. Or topmost element is maximum and its children are smaller than the node. It is used when less time is required because it can perform operations like insert, delete or search an element with less time complexity. ",1.0
-4044,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",there are three types of heap binomial heap fibonacci heap and binary heap,2.0
-4045,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a graph can have spanning trees equal to the number of nodes of the graph.,2.0
-4046,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap is a data structure which stores minimum element as root and then subsequent child nodes,2.0
-4047,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap in a data stucture is tree,2.0
-4048,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a data structure which is of two kinds a min heap or a max heap. In a min heap the parent element is always supposed to be smaller than its child nodes. ,2.0
-4049,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a useful data structure when its necessary to repeatedly remove the object with the highest (or lowest) priority or when insertions needs to be interspersed with removals of root node.,2.0
-4050,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","There are two types of heap one is min heap and the other is max heap, in min heap the sum of the child nodes is greater than the value at parent node and in max heap  the sum of the child nodes is less than the value at parent node .",1.0
-4051,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure in which we change the indexes of the elements of the array according the type of heap i.e min or max heap.\n,1.0
-4052,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,
-4053,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure wherein the parent is either always larger or always smaller than its child nodes.,1.0
-4054,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",There are two types of heaps . First min heap where the root node is less than the child nodes and the second max heap where the root node is greater than the child nodes.,1.0
-4055,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap arranges data in form of tree but the elements in this tree is based on the type of heap. if it is min heap the root  or parent is always smaller then its children. And if max heap then root or parent is always greater than its children.,1.0
-4056,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is a data structure which is a binary tree in which either we have the root the largest element and then the next smallest on level 1, or the smallest element on the root and then the just larger one on the level 1 and so on depending upon our requirements.",1.0
-4057,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is a kind of tree whish is in sorted form called min heap or max heap. heap carries out operations like sorting, searching, insertion, deletion.",1.0
-4058,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure is a type of data structure which stores the minimum or maximum element at the top. This data structure is generally used implemented using arary while the indexig is assigned in such a way that it behaves like a binary tree.,1.0
-4059,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap is a data structure that stores the data in tree format either in increasing order where root element is smallest i.e minheap or it stores it in descending order (maxheap)where root is the greatest element.,1.0
-4060,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-4061,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","it is used to store data, we can create min and max heap which is arranges in the ascending/descending order",1.0
-4062,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Non linear data structure. ,1.0
-4063,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",edges-1,1.0
-4064,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap in a data structure is a non-linear data structure, in which n number of nodes can be linked to its parent node.",1.0
-4065,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a container like structure used to store array values or integers,1.0
-4066,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a data structure in which elements are stored in form of a tree. it has two types - min heap and max heap. either the min or max element is maintained on the top and elements as we go down the tree follow the same property. While inserting, deleting, we need to maintain them.  These are useful to store and access min and max elements when need be.",1.0
-4067,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a balanced binary tree whose root node is its key node and is compared with its children nodes.,1.0
-4068,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a balanced binary tree where root node key is compared to its children,1.0
-4069,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",It is the balanced binary tree data structure where the root node key is compared with its children and arranged accordingly.,1.0
-4070,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly.,1.0
-4071,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a data structure where we can traverse the complete binary tree as well as it is an  area in  computer  for main storage of memory.,1.0
-4072,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-4073,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is to maintain a large amount of data , heap can also is called a tree \nit stores the data ",1.0
-4074,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",It is a type of data structure which ,1.0
-4075,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A Heap is a special tree data structure in which the tree is a complete binary tree.,1.0
-4076,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a type of data structure in which a tree is the binary tree and it is use to store global variables.,1.0
-4077,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a tree based data structure which is a complete binary tree that satisfies the heap property.\nHere  a root node is compared with its children and then it is placed accordingly depending on whether it is a min Heap or a max Heap. ,1.0
-4078,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a non linear data structure in which the root node is always maximum or minimum value and is maintained throughout the data strucutre ,1.0
-4079,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure is a tree based data structure either a maximum elements are at top and lowest at bottom or vice a versa.,1.0
-4080,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a tree which always have its minimum or maximum element at its root or a tree in which a parent node is always bigger/smaller(max heap/ min heap) than a given child node. It is a complete binary tree,1.0
-4081,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap data structure is a type of binary tree which follows either of the 2 rules. Either a child can not be greater than the parent . Heap formed in such a way is called Max Heap. or the parent cannot be greater than a child , such a heap is called Min Heap.",1.0
-4082,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is defined as special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly,1.0
-4083,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a tree based data structure where either the minimum elements are at the top and max at the bottom or vice versa,1.0
-4084,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a data structure which is a Binary tree and it also follows the complete binary tree property. There are teo kind of heaps:\n1. Min heap - It stores the minimum element at its root and follows CBT property.\n2. Max heap - It stores maximum element at its root and follows CBT property.,1.0
-4085,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a kind of data structure which stores the data. There are various types of heap one of them is binary heap.,1.0
-4086,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure to store data in minheap form and maxheap form. eg in minheap every children is greater than its parent.,1.0
-4087,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A Heap is a special Tree-based data structure in which the tree is a complete binary tree.\nThere are two kinds of heaps, minimum heap and maximum heap. ",1.0
-4088,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure in which the value at a node is smaller than the values at its children and the smallest element is kept at the top of the tree and the tree can be represent using an array where the child of ith node are 2*i and 2*i+1,1.0
-4089,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structure is basically a tree having the root node is max or min element of the entire tree depending upon the heapify function\nIt is the special tree because every sub tree node is the max or min value of that tree that help us to find the kth largest or kth minimum element of that tree,1.0
-4090,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a special tree based data structure in which the tree in completely binary tree and it is of mainly 2 types \n1) min heap - min element is at the top \n2) max heap - largest element is at the top,1.0
-4091,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","AVL tree is a self balancing tree where in we implement the data structure i.e. the tree in such a way that whenever an element is inserted into an AVL tree, it balances itself before any other operation can be completed on it.  ",1.0
-4092,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly. If x has child node β then − key(x) ≥ key(y) As the value of parent is greater than that of child it is called Max Heap. Suppose parent is lesser than that of child it is called Min Heap.,1.0
-4093,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A type of storing data structure that stores the data in two patterns - min Heap and max Heap. It is in our power to store data in the form of parent nodes, and children nodes. It is basically a tree that has been modified in a way that we make any parent node to have children that are only smaller/larger than the parent node. This storage is called a heap, also priority_queue.",1.0
-4094,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap is a data structure in which Array of elements represented as a complete binary tree. It is of two types Min-heap and Max-heap.\nIn min-heap the root node of the heap is the smallest element and all the parent elements are smaller than their children.\nIn Max-heap the root element is the largest element in the heap and the the parent nodes are greater from their children.,1.0
-4095,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a other name of priority queue in which element are store in form of a binary tree (implemented with use of array)with child nodes and parent with elements having their priority priority order for eg. min heap ,max heap.",1.0
-4096,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap in a data structure is used to represent elements\nIt is of two types:\nMin heap:In this type,the parent element is smaller than the child.\nMax heap:In this type,the parent element is bigger than the child.",1.0
-4097,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",It is a data structure in which the elements are stored in the form of binary tree.\nIt is of two type MIN Heap and MAX Heap.\nIn MIN Heap minimum element is present at the root .\nIn MAX Heap maximum element is present at the root .,1.0
-4098,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","A heap is a data structure that resembles a tree but infact, is multiple trees combined in a minimum/maximum fashion.\nA Minimum Heap (or a MinHeap) is a heap which has the minimum number/value at its root node, and arranges the rest of the values in increasing order towards the leaf nodes.\nA Maximum Heap (or a MaxHeap) is a heap which has the maximum number/value at its root node and arranges the rest of the values in decreasing order towards the leaf nodes.",1.0
-4099,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap data structure is build by using priority queue and is of two types-:\n1-min heap-in this heap minimum elements is placed at to top or root node it is build using priority queue.\n2-maxheap-in this heap max element is place at the top.,1.0
-4100,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","elements are arranged in the form of binary tree having two ways to do it, namely min heap and max heap. In max heap highest value element is placed on the root node and in min heap lowest value element is placed at the root node",1.0
-4101,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","AVL tree is a type of binary search tree. Rotations are performed on it, to sort the elements. The rotations involve-\n1. LL - left left\n2. RR- right right\n3. LR- left right\n4. RL- right left",1.0
-4102,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure in which we use array to store data . it is a complete binary tree and it is of two type max heap and min heap.,1.0
-4103,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is data structure that stores element in the form of an array but we visualize it as a complete binary tree. There are two types of heap in general i.e. min heap and max heap.,1.0
-4104,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is a type of data structure in which we can construct various types of heaps like min-heap,max-heap,etc. The min heap provides a data sorted in decreasing fashion and max heap provides a solution in ascending order.",2.0
-4105,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","heap is a data structure used to store information on top of the given information. in max heap, the maximum element will be the parent node and in min heap, minimum element will be parent node.",2.0
-4106,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",In head the data is stored in position which is determined by a formula there are two types of head-\nmin head\nmax head,1.0
-4107,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is dynamic structure it has two variant min heap and max heap it can be transferred in an array.,1.0
-4108,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a binary tree where the value stored at parent node is always greater than the value at child node,2.5
-4109,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is similar to tree but it contains information in two formats - \n1. Max Heapify - In this case root node should hold the maximum value and every parent node should be greater than its children node.\n2. Min Heapify - In this case root node should hold the minimum value and every children node should be greater than their parent's node.,2.5
-4110,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a tree like data structure in which node are arranged in either decreasing order or in increasing order based on which heaps are named as minheap and maxheap respectively. if we do the level order traversal of a minheap or maxheap we get values arranged in ascending or descending order respectively,2.0
-4111,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a dynamic data structure it has 2 variants minheap and maxheap it can be traversed in form of array.,1.0
-4112,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a Data structure which stores memory value of the variable it can be used for pointers There are two types of Heap Data Structure first one is MIn Heap and the other one is Max Heap.In Min Heap our Root value of the tree must be of minimum value and in Max Heap root value of the Tree must be maximum ,2.0
-4113,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-4114,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap tree holds data such that values to greater than parent node goes to the right node and values smaller goes to the left of the parent node.,0.0
-4115,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap data structure is used to store data in such a way that left of root shoulld always be smaller and right of root should always be greater. In Heap Root should always be the smallest element.,0.0
-4116,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure in which max/min element is on top and then accordingly a tree is then formed,1.0
-4117,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","it is different type of data structure made up by using trees and can be considered and extension to trees. it is of many types such as binary heap , binomial heap etc. ",0.0
-4118,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly. Heap in data structure can be of two types min heap and max heap.,1.0
-4119,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap data structures can be visualized as a binary tree where the top most node value can be maximum or minimum among all decided by the type of heap. Heap can be of two types Min-Heap and Max-Heap.  In Min-Heap min element is at the top and vice-versa in Max-Heap.,2.0
-4120,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-4121,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap in data structure always stores the least or greatest element at its top, so its used when we need to extract minimum or maximum element.",2.5
-4122,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a non linear data structure,1.0
-4123,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",a heap is data structure is which remove the object when it is on higher or lower priority ,1.0
-4124,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap data structure consists of parent node and child nodes forming a heap like structure ,1.0
-4125,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap in data structure means that the value of parent node is bigger than its left and right child.  We can create two types of heap :- Max heap and Min heap . This data structure is more useful in deletion , insertion etc.",2.5
-4126,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a type of data structure in which elements are arranged in such a way that it follows min-heap or max-heap property according to the requirement of the problem.,2.0
-4127,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is non linear data structure . which is working in both form array or linked list. ,1.0
-4128,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",in heap every element is stored in a form of reference each element has a reference to it to get identified ,0.0
-4129,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Heap is a form of data structure where data is arranged in smaller heaps and grouped to form the bigger heaps. There are two types of heaps, Min-Heaps and Max-Heaps, Min-Heap being in the form such that root is the smallest of the whole heap and same rule is followed throughout the heap while Max-Heap is just opposite.",1.0
-4130,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heaps are non linear data structure used to store the data,1.0
-4131,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",there are the two type of heap :\n--> MinHeap(minimum element  on a root node)\n--> MaxHeap(maximum element on root node),1.0
-4132,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a data structure which stores the data of given information or array,0.0
-4133,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap or priority queue is a data structure can be used under hoffman encoding algorithm,1.0
-4134,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",Heap is a non-linear data structure used for storing data. There are two types of heaps: i) MinHeap & ii) MaxHeap\nWe can perform 3 operations on heap: a) Heapify (build heap)    b) Insert  c) Delete                ,2.0
-4135,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","a heap is a data structure in which we have data in for of a heap, we use min heap and max heap using heap data structure. ",1.0
-4136,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",one element added in accordance to its parent and left child and right child then operations are performed respectively if one has to perform minimum heap serch then smallest node is present at the top and if max heap perform large element present at top ,1.0
-4137,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is type of dynamic data structure ,0.5
-4138,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",A heap data structure is a modification of the tree data structure in which the children of each node are either larger than their parent node or smaller than their parent node.,2.5
-4139,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a type of data structure use to  minimize the size of tree. For example Heap Sort.   ,0.0
-4140,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is tree like structure used to store data  in tree manner having nodes.,0.0
-4141,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",,0.0
-4142,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",there are two types of heaps: min heap and max heap.\nit is a binomial tree which follows heap properties.\nwe create a priority queue and heapify it.,1.0
-4143,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is a method to store data in data structure it is optimizer and has less time complexity ,0.0
-4144,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","Its a special type of binomial tree, which follows heap order properties.\nA heap can be - min heap or a max heap ",0.5
-4145,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is min element or max element provide top or bottoms it depend on which type of heap is ,2.5
-4146,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.","basically, heap is a binary search tree.",0.0
-4147,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is type of data structure which is dynamically created and properties of a binary search tree \nit is of two types \n1)min heap\n2)max heap ,0.0
-4148,What is a heap in data structure?,"Heap is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly. A min-heap, a parent node has key value less than its childs and a max-heap parent node has value greater than its childs.",heap is basically a data structure where we stores the value or data  in a given problem.,0.0
-4149,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is one which calls upon itself till the base condition is satisfied.,2.0
-4150,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","recursive function is the function which calls itself again and again multiple number of times for example in fibonacci we call a recursive function fib(n,n-1);",2.5
-4151,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function can be defined as a routine that calls itself directly or indirectly when needed in a program ,2.0
-4152,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Recursive function instead of using loops to move forward to next subproblem, calls itself again with same or different arguments",2.5
-4153,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function that calls itself until a certain base condition is met. When the base condition is met it returns a value which goes back to the previous function call. ,2.5
-4154,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive Functions are used to reduce time complexity and easy to use when we have to call same function again and again for its subproblems.\nRecursive functions are used to memorize value for its subproblems. ,2.5
-4155,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",it is a  concept or property of one or more variables which is specified by a procedure that yields value or instant of that function by repeatedly applying given relation .,2.5
-4156,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is an function which can call itself .,2.0
-4157,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is the those function which calls themselves repeatedly to solve the problem\nthey have a base condition to stop the recursion\n\nalgo recursion(int n)\n{\n     base condition\n      if(n>-1){\n          sum=sum+n;\n         recursion(n-1);\n       }\n       return sum;\n}\n,2.0
-4158,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function can be defined as a routine that calls itself directly or indirectly.,0.0
-4159,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is basically a function which works on divide and conqueror. In this divide the problem until we could find the solution of the same smaller problem and hence we try to find the solution for the big problem.\nEx- to find factorial of a number ,2.5
-4160,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function that calls  the function itself  to get the solution of the problem and returns the value .,1.0
-4161,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function in which it compares the given loop or function again and again till it get the perfect outcome.,1.0
-4162,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is the one that calls itself again and again to solve the given problem.,1.5
-4163,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",the function which depending upon a certain condition calls itself is called recursive function.,2.5
-4164,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",It is function which call itself until the exit condition satisfy.,2.5
-4165,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",any function that calls itself repeatedly only till the point a certain condition is true is called a recursive function,2.5
-4166,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",,0.0
-4167,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is the function which calls the function again until a required value is obtained .it is combination of three steps determining the base cases forming the recursive relation and returning the solution .it is backbone of dynamic programming,1.0
-4168,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which repeatedly used a set of lines to get a certain output.,1.0
-4169,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.", nodes .,0.0
-4170,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function in any coding language is a function that can call itself again and again , it could be directly or indirectly until the program satisfies a specific condition.",1.0
-4171,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Function which call itself within their own function is called as recursive functions. It find the minimum value of that function which can be used to solve the whole function .\nIt could be a decreasing function or a dividing function which can used to find out the minimum value that function can hold.It can be done by making state space trees  or using masters theorems. ,2.5
-4172,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Heap is a data structure which is similar to a complete tree.  It is usually used to store data in a particular order. It is useful when a set of variables are not fixed and is changing with each step in an algorithm. There are two types of heaps:\ni) minheap (root node has the minimum element)\nii) maxheap (root node has the maximum element)\nExtracting elements from these heaps gives elements in sorted manner (ascending or descending depending on type of heap). ,2.5
-4173,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","recursive function is a function with call itself many time, it needs extra stack size while  running recursion.\ncontinues call , ",2.5
-4174,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which is called again and again each time on smaller part of the array or list or tree nad each time it waits for the answer from the smaller function is recursive function.,2.5
-4175,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which calls itself in a loop is called a recursive function.,2.5
-4176,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","When a function calls itself within a loop to find a solution, it is called a recursive function.",2.5
-4177,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function which is called inside itself with smaller parameter. it is a function which find the solution to a given problem by dividing it into several sorter problem. If always contain base case which is always used to find solution.,2.5
-4178,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function that calls itself and terminates when a base condition is met.,2.5
-4179,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a function that can be called number of times that is again and again within the same program, instead of writing the function again and again we call that function many times as per our requirements. Such functions have a specific function which is required to perform on several number of elements which can be taken as input in the array such as each element of the array can be checked whether it is prime or odd or even etc.",2.5
-4180,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function means calling the same function multiple times until we don't gate the desired output.\n,2.5
-4181,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function which calls itself more than once . calling can be outside function or inside function (direct or indirect).\nthe main components of recursive functions are :-\n1-base case where the function gets terminated.\n2-recurrence relation \nand then some optional operations .,1.0
-4182,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Recursive function is a function to be called again and again infinite number of times to execute until u get the desire output. so instead of calling function again and again implementation of recursive function can be of help examples-to find odd number ,to find prime numbers etc.",2.0
-4183,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is one in which we just make a formula for one case and rest other cases are automatically implemented by that one formula by making a function. \n,1.0
-4184,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a call to a user built function inside the function itself. This is used to make a loop of the given steps inside the function until a desired output is returned.,2.0
-4185,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which has has base terminating condition else it keeps calling itself again and again till the terminating condition is satisfied.,2.5
-4186,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Its a kind of function which calls itself in the function. It helps us to solve the huge problems through shorter methods. A recursive function is a kind of function which helps the us to code the program in shorter length. ,2.5
-4187,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A graph can have a maximum number of spanning trees equal to its vertices.,2.5
-4188,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is as function which includes a call to itself. It comes out of the loop using the base conditions provided.,2.5
-4189,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A function calling itself is known as recursion.\nIf there is a call to a function, within the function itself, then it is known as recursion. It can either pass different values or same values as well (but passing same values will be of of no benefit and it will stuck in an infinite loop).\n\n// Recursive call for this question: If uou didn't understand recursion, read the answer again",2.5
-4190,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A function which comprises of a base condition, code and a call for itself with increment or decrement in its values to recur and finally reach the base condition and return the desired value is called a recursive function.",1.5
-4191,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function that runs recursively till  all the possible solution of a given problem are found. Recursive function is really beneficial as you don't have to write multiple a line of codes. consists of for loops.,2.5
-4192,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Heap Data Structure is of two types-\n- MINHEAP: The parent node is the smallest and the next smallest element is inserted as the left child. \n- MAXHEAP: The parent node is the largest and the next largest element is inserted as the left child. ,1.5
-4193,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which can call itself again and again in a program is known as a recursive function . ,2.0
-4194,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is used in the problems where a bigger problem can be divided into similar sub problems so that we can construct a similar function for that problem and break the inputs in smaller form. Recursive functions improve the reusability of the code .,2.5
-4195,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive Function is a function that calls itself in the body of function until the base condition is  not executed.,2.5
-4196,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls it recursively till the given required condition is fulfilled.\n,2.0
-4197,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls upon itself repeatedly in order to perform a certain task.,2.0
-4198,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",The function use repeatedly in the program or the function which again called itself are known as recursive functions.,2.0
-4199,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Recursive function is defined as a kind of function written in the program that calls itself again and again without user inputting the value each and every time. It is also shown as function calling function. Ex:binarySearch(arr,arr[mid-1],key) and binarySearch(arr,arr[mid+1],key)",1.5
-4200,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function which is calls itself which some bases cases to terminate it.,2.5
-4201,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a function that calls itself repeatedly until a base case is met. It involves breaking down a problem into smaller sub-problems that are identical in nature, and applying the same function to those sub-problems until the base case is reached. Once the base case is met, the function stops calling itself and returns a value. ",2.5
-4202,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function that calls into itself.,2.5
-4203,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function that calls itself repeatedly until we have reached the end of list to find the desired output.,2.5
-4204,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is one which calls itself when certain mentioned conditions are met.,2.5
-4205,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function which calls itself within its own function,2.5
-4206,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function which calls upon itself. ,1.0
-4207,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is one that calls itself. It occupies internal stack space,2.5
-4208,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function which calls itself to reduce the space complexity and duplication for solving any question.,2.5
-4209,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function that calls itself ,1.0
-4210,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",It is a function in which the function calls itself in it \n,2.5
-4211,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",The function which call its itself multiple time till the base is true.\nThere is base,2.5
-4212,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Suppose a program has a specific set of code that need to be repeated more than once. in such a case we make a separate function for this code and call it recursively whenever needed. basically we are dividing the code into smaller parts and solving the problem,2.5
-4213,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function that we made for the purpose of repetition since we found out it solved problem for one of the cases effectively and it could be used for all of them repeatedly.,2.5
-4214,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function that can be called multiple times throughout the code,2.5
-4215,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that keeps on calling itself repeatedly is called a recursive function.,2.5
-4216,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is that a recursive call itself whenever the conditions in not satify . But the complexity of the recursivce algorithm is higher than the other algorithm .SO that less use of recursive algorithm compare to the other algorithm.,2.5
-4217,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",it function that calls itself in its return value ,1.0
-4218,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is one that calls itself again and again while a certain condition is fulfilled. It is often used in divide and search algorithms. ,1.0
-4219,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which calls itself is called recursive function.,2.5
-4220,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is function that keep on calling itself until it reaches the termination condition.,2.5
-4221,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which calls itself is called a recursive function.\nEg.\nint fact(int n)\n{\nif(n==1||n==0)\nreturn 1;\nelse \nreturn n*fact(n-1);    // here function is calling itself\n},2.5
-4222,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","a recursive function are a set of operations that we want to be repeated untill and unless the break case is met.\nit consists of the function body,the function arguements through which the iterative procedure will take place and a break case that will end the iteration.",2.0
-4223,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",When we call the same function inside a function it is called a recursive function.,2.5
-4224,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function which calls itself i.e it has a call with in its body,2.5
-4225,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a self calling function that takes its previously generated value in itself until the termination condition is reached.,2.5
-4226,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.", A recursive function is a function which calls itself again and again till it reaches  a terminating base condition after which it backtracks to give the result \ntwo parts\n1 base condition\n2 recursive call statement,2.5
-4227,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","When a function calls itself and uses its previous function to define itself, it is called a recursive function. ",2.5
-4228,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function that calls itself is called a recursive function ,2.5
-4229,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",which all the function again and again until the condition is satisfied to come out from the loop,2.5
-4230,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which calls itself is a recursive function,2.5
-4231,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function which has a base case which is a pre defined case and a recursive case which calls the function in it self.it is an example of divide and conquer only.,2.5
-4232,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function that calls itself repeatedly until the given base condition if fulfilled.,2.5
-4233,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function calls again n again itself until the function reaches the base condition,1.5
-4234,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function that calls itself,2.5
-4235,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is one that calls itself.,1.0
-4236,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is the call to itself i.e. if we call a function with same name and parameters inside a same function that is the recursive call of that function.,2.5
-4237,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function which calls by itself.,1.0
-4238,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Any function that calls itself again and again is called recursive function.,1.0
-4239,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is the function which calls itself using different inputs until result is returned.,2.5
-4240,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls itself from within itself,2.5
-4241,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function are the operation in which we made a function and we call that function in the function which we made.,1.0
-4242,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",its a type of function which call itself within the function for example merge sort,1.0
-4243,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function that calls itself repeatedly is called recursive function,2.5
-4244,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself is called a recursive function.\n,2.5
-4245,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive functions are basically those functions which can be repeatedly  called.,1.0
-4246,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Heap is a Binary tree in which the parent node always contains greater or lesser values than their child nodes, if it contains greater values than the child nodes then it is max heap and if contains less values then it is min heap.",2.0
-4247,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",There are two types of heaps Minheap and Maxheap,2.5
-4248,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls itself whenever it is executed as if in a loop. To break out of a recursive function we define a base condition. Example:\nfunc1(){\nfunc1();\n},2.5
-4249,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",calling of a function inside a function refers to a recursive function\nwe reduce the code written by us and call that function with some parameters\naccording to our need.,1.0
-4250,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a type a function in which function call itself\nvoid  func(int n){\n// code\n \n func(n-1);\n\n//code\n},1.0
-4251,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","recursive function calls back the same function which reduces the code complexity and increases understandability of the code.\n\nfor ex - \nvoid dfg(char c , int n)\n{\nn= n/2;\ndfg(c , n-1);\n}",2.0
-4252,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function which is used to solve a given problem by repeatedly by applying a given relation to known values of the function.,2.0
-4253,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",function which can repeat itself on the basis of base case arrival.,2.0
-4254,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",when it call it self again and again until it base case is achieved.\n,2.0
-4255,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function that calls itself multiple times. The function gets called in a program many times until the base case gets hit. This stops recursion.,2.0
-4256,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Recursive function is a function which calls itself again and again untill the base condition is satisfied. It uses the stack data structure to store the previous operation and first performs the next recursive call, and keeps on doing the same thing till it reaches the base condition and then performs the operation stored in the stack.",2.0
-4257,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function are functions in which the function is called within that function again and again until some base case is reached.,1.0
-4258,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is defined as calling the function again and again inside itself until the base case is reached.,1.0
-4259,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Recursive function is a function called in itself and a smaller input is passed after every call. A base condition is specified . When the base condition is reached, the function returns a value which is used in the previous recursive call.",2.0
-4260,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",it is a function inside a function .\nit keeps on executing or unwinding until the base case is achieved.,2.0
-4261,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a function that after a certain block of code, and with a newly calculated data set runs the same function inside the function. This is used when we calculate something, but now wish to repeat that same process for the final calculation on the newly derived data.",1.0
-4262,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Heap is a data structure in which data is stored in tree form such that it follow the properties of binary search tree and elements are always filled from left to right.,1.0
-4263,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function that calls for itself repeatedly till the base condition is reached.,1.0
-4264,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",it is a type of function where each problem is further solved by solving subproblem. ,1.0
-4265,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which keeps calling itself within itself till it reaches a base case and hence forth compute the value that was primarily required.,1.0
-4266,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Heap is a data structure in which ,1.0
-4267,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function that calls upon itself to solve a smaller part of the same problem and stops calling itself when the base condition is met.\n\n3 parts to a recursive function:\n1. Base case\n2. Recursive call\n3. Small Calculation \n,1.0
-4268,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",It is a type of function which calls itself in a function,2.0
-4269,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Recursive function is a function that calls it every time till the base case condition is reached. In recursion technique, we only need to solve one case and other cases are solved by recursion itself.",1.0
-4270,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function which calls itself unless base condition is achieved.,2.0
-4271,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.", recursive function is the function which call itself in the function itself to solve smaller piece of information repetedly\nit is used for to make algorithm run faster and space efficient,2.0
-4272,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is one which is called by itself in its definition again and again until the base condition is reached. ,2.0
-4273,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","avl tree is a balanced binary tree in which left node is less than the parent node and right node in greater than the parent node, and then rule applies to the whole data structure.",1.0
-4274,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself within itself is called a recursive function. ,1.0
-4275,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is used when the problem has repetitve type of problem which requires changing the parameter,2.0
-4276,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls itself for performing a task it consists of atleast two things:\n1. limiting condition\n2. recursive calls,2.0
-4277,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",function that are continuously calling itself until base condition is reached is called recursive function.,1.0
-4278,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function which keeps on calling itself with different values till the solution for the function is found.\n\nEx Fibonacci(2) =  Fibonacci(1) + Fibonacci(0)\n  Fibonacci(1)  = Fibonacci(0)\nFibonacci(0) = 1 ,1.0
-4279,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Heap Data Structure ensures that the element present as the root element is the smallest (in case of min-heap) or greatest (in case of max-heap).,2.0
-4280,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","recursive function is very important and used in so many function call , \nbasically  in this we create a recursive function , recursive means that it is calling the same function again and again  until all condition is not checked ",1.0
-4281,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function uses recall to divide a problem into parts and is stored in the form of a stack as the fisrt output is released at the last. \nit is a great problem solves instead of  using loops and saves time .,2.0
-4282,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",defined calls directly and indirectly.,2.0
-4283,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function made by us that calling again again until it did not break or return zero that function is called recursive function,1.0
-4284,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a function which calls itself inside itself, that is the function will keep calling it in the recursive case, until it reaches the base case when it terminates.",1.0
-4285,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function which calls itself means calling the function in the same function.,1.0
-4286,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls itself again and again till the base case has reached and the solution of the base case is known,2.0
-4287,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is the function that calls itself.,2.0
-4288,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function which keeps calling itself for a repeated number of times,2.0
-4289,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a heap is a type of graph in which elements are arranged in such a way that either each root element is greater than its children(max heap) or is less than its children(min heap).,1.0
-4290,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function which is called within itself.,2.0
-4291,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls itself .,2.0
-4292,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function in a function calling itself in its body till a particular condition is met. This is often used to replace loops. ,2.0
-4293,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is calling the same function inside the function. Its very effective in solving the problems which otherwise are very difficult to solve iteratively.,2.0
-4294,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive functions are the functions which call themselves repeatedly .,1.0
-4295,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls its ,2.0
-4296,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Heap is a data structure which is used to store elements.,2.0
-4297,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is one which calls itself based on the need of the code including a base case for its termination. recursive function usually are used to reduce the problems from a large problem into a short problem,1.0
-4298,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a function which finds the solution recursively , by defining a base case. The function is called again and again. ",2.0
-4299,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",It is a function in which we declare termination conditions and then call the same function again and again until we get our result.,2.0
-4300,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function where we solve a particular problem using the function itself.,2.0
-4301,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","a recursive function is a function which is called again and again and it carries out the operations defined in it, until some specified condition is achieved.",2.0
-4302,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a function which calls either self or another function inside itself. This creates a loop and hence we need to create a base case to stop the recursion,",2.0
-4303,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function variables and constants used the the represent the time complexity of the recursive algorithm\neg T(n)=2T(n) + C,2.0
-4304,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","In recursive function ,we call our original function within the function by changing the values of original arguments so that we obtain an optimized solution\nWe can say we divide the problem into subparts and then obtain the final result .",2.0
-4305,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",it is a function which calls itself inside the function until it reach its base condition.,2.0
-4306,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls itself repeatedly to execute a particular task. ,2.0
-4307,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",,0.0
-4308,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a one which calls it itself again and again until a condition is not satisfied. It uses the stack technique for the function call, and accordingly gives the output.",2.0
-4309,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function used when a same procedure is to be followed again and again for a large number of times so it recalls itself again to find the sollution ,2.0
-4310,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function takes the input and reduces it to generate the output in each iteration. It is used to perform repetitive functions. Example to calculate Fibonacci we have to again and again add the previous numbers sum so we can create a recursive function that would keep doing it for us until a base case is reached which leads to termination of the function. ,2.0
-4311,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself within its own function body is called a recursive function.,2.0
-4312,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function are function that call itself.\nafter each call the problem is reduced until a base value is found.,2.0
-4313,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",The function that calls itself in a programme is called recursive function.,2.0
-4314,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","When a function calls itself and uses its own previous terms to define its subsequent terms, it is called a recursive function",2.0
-4315,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",In recursive function we create a base case and functions are created in such a manner that using them in code recursively calls the function and performs the operation mentioned in the function recursively,2.0
-4316,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function  is a pre defined function that can be used in a program when we use a particular library that already specifies its uses and functionality ,2.0
-4317,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive nature function is function which is used to minimize the time complexity by calling the function once rather than calculating the formulas again and again.\nMostly the recursive function are used in dynamic programming to provide the best optimal solution for the question\n We create a function which we know that is to be used very much so rather to provide a good complexity we create a recursive function according to the question,2.0
-4318,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",When a function can be use and can be called recuursively(i.e again and again) in a program we called it reccursive function.,2.0
-4319,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.", a recursive function can be comprehended as a system that calls itself directly or indirectly.,2.0
-4320,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function in which function call itself again and again. and it is also use to reduce time complexity and memory.,2.0
-4321,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function which call itself to perform an operation. It can also be said that recursive function and stack are analogue to each other.,2.0
-4322,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function that calls itself until a terminating condition called base case is reached is called recursive function,2.0
-4323,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function calls itself while the condition given in the main function or in it is true.,2.0
-4324,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself somewhere in its a algorithm is called a recursive function.\n,2.0
-4325,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is function which is called in itself .  A recursive function has a base case condition and call of same function to solve a smaller problem . Eg\n factorialofnumrecursive(int n)   {\nif (n==0) \n{\nreturn 1;}\nelse \n{return n* factorialofnumrecursive(n-1);}\n,2.0
-4326,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is if any function is called multiple times in a particular algorithm the we called it a recursive function which is called recursively.,2.0
-4327,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a type of function which we use when we know that we have to do a type of operation many times.\nit is a function which checks the statement and  calls itself repeatedly accordingly,2.0
-4328,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function is a kind of function which calls itself to evaluate the answer, its calls are stored in stack , it also generally contains a base case to avoid infinite calls.",2.0
-4329,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function helps the user not to write the same piece of code again and again. It saves much time .\nUser only have to do a recursive call to the function and it executes till the condition is true.,2.0
-4330,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","recursive function is a function which calls itself again and again untill it reaches the base condtion,",2.0
-4331,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function in which the same function is called again for a smaller sub-problem of the same kind.,2.0
-4332,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function that keeps calling itself and is terminated by a base condition,2.0
-4333,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is that function that call itself again in it \nsome examples are merge sort and quick sort,2.0
-4334,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is manily made by calling the function itself in the funnction to make the possible recusion calls to make the algorithm and in this we make the base case also to stop it from the infinity call and to get the anwser,2.0
-4335,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A graph can have multiple spanning trees, the most number i.e. possible for a graph having N nodes is N spanning trees. ",2.0
-4336,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is function in which it calls itself until a base case condition satisfies.,2.0
-4337,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A recursive function - If a function is calling itself to perform the same repetitive operations only until it reaches the base case, when it can stop the recursive call and return back to the initial recursions. This is known as recursion. We can get a recurrence relation based on the calls and how the function is behaving.",2.0
-4338,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which is called again and again in itself. It has a terminating condition in it which stops/return a value on  satisfy a particular condition.,2.0
-4339,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function refers to a function which call itself again and again till the base case is satisfied.,2.0
-4340,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","A function that calls itself is a recursive function.The function may call itself a no. of times depending the base case.Base case indicates when a recursive function has to stop calling itself,otherwise,it undergoes an infinite loop.Usually base case is defined and has a definite value.Recursive functions are used to solve big problems by solving similar smaller problems first and then combining these solutions to get the required solution to the bigger problem.",2.0
-4341,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",It is a function in which the function calls itself for a specific number of times until a base condition is satisfied.,2.0
-4342,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function that keeps calling and recalling itself until the problem is solved. Used to reduce repetitions in code.\nMay make the code less or more complex depending upon the Data Structure and approach used to implement etc.,2.0
-4343,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursion is use when we have repetitive type of sub problems\nrecursive function is a type of function in which we define a base case edge case(if any) and work on smaller sub problem of that problem and call recursion for rest of the problem,2.0
-4344,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",it is a function which call upon itself until the base condition is achieved,2.0
-4345,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",,0.0
-4346,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function which calls itself. it has a base case at which the recursive calls terminates. it works like a stack. the problem is divide into subproblems and the recursive function is called on that subproblems.,2.0
-4347,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function  which calls itself for other cases and forms a stack and calculates solution accordingly. It breaks problem into sub problems and solves the smallest sub problem with the help of base case provided in order to solve the main problem.,2.0
-4348,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a type of function used when we have a problem which can be solved by calling itself.basically a function which calls itself until a desired condition is met is known as a recursive function.,2.5
-4349,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function which calls itself multiple times within itself,2.5
-4350,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",In recursive function a function call itself and it will stop after meeting stopping condition.,2.5
-4351,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is that they call itself and calling till the given condition if not fulfilled.,2.5
-4352,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","a function that calls itself, when the base condition becomes true. it can be used to make iterative tasks easy.",2.5
-4353,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function in which we identify a base case and calls the same function again and again is called recursive function.,2.5
-4354,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive functions are those in which a function calls itself again and again till it reaches a base condition and  from there it starts backtracking.,2.5
-4355,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function make calls to itself until an edge case is reached and then returns the final answer by retreating back by calculating the answer in whatever form.,2.5
-4356,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recusive function are used in Dynamic Programming these are the type of functions which call itself ,2.0
-4357,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a type of function which is call repeatedly in a program when program stuck at a point it goes back to function and call it again,1.0
-4358,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself.,2.5
-4359,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive functions are the functions which call it self again within it's own function in order toi repeat the same procedure again and again untill a specific base condition is met which breaks the recursive call.,2.5
-4360,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function in which the function is called within a function but with diff para meters,2.0
-4361,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",it is a function where there is an call to the function in the function itself.,2.5
-4362,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A Recursive function is a function that calls itself again and again to give the final output. A recursive function is a function that uses itself to breakdown a more complex problem to give final result.,2.5
-4363,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.","Same Function call within the given function.\nA recursive function  has three components the base case, Inductive Hypothesis and the output. ",2.5
-4364,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",function who call itself ,2.5
-4365,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself again and again with smaller inpus until it hits a base condition is a recursive function.,2.5
-4366,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is which keeps calling itself over and over again repeatedly.,2.5
-4367,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function that call itself with smaller value and return itself with large value,2.5
-4368,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is one of the way to make your solution more efficient and  is used to make your problem approach in a minimal lines and reducing the time complexity ,2.0
-4369,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function which calls its itself again during program execution is called recursive function . ,2.5
-4370,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function is a function that calls itself repeatedly untill we reach the end.,2.5
-4371,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursion  is defined as the function which is depend on call by reference.,0.0
-4372,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",for general code we use a loop but for bigger statements we use a recursive function to perform the problem in recursive function we specify the operation and provide a number of iteration and provide a break point.  ,0.0
-4373,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself is called a recursive function. It is provided with condition statement such that it ends as soon as the condition is fulfilled.,2.5
-4374,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a function call without  main function is known as recursive function,0.0
-4375,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",,0.0
-4376,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",In a recursive function is function in which a function call itself inside a function,2.5
-4377,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",function which calls itself in a same program or block repeatedly,2.5
-4378,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function calls itself until base case comes.,2.5
-4379,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself.,2.5
-4380,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",functions which are made by using return function is called recursive function,0.0
-4381,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",,0.0
-4382,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A recursive function is a function that calls itself,2.5
-4383,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",Recursive function are those function which is define to perform recursive operations.,2.5
-4384,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function that calls itself repeatedly.,2.5
-4385,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",the recursion of elements in function known as recursive function ,0.5
-4386,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",a recursive function is a function that has its own self in its structure.\nIt paradoxically calls itself again when the recursive function is called.,2.5
-4387,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a technique which is used to call a data form data base recursively or we can say reverse ,2.5
-4388,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",A function that calls itself wherever it is mentioned in the main function.,2.5
-4389,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is call self again and again when base condition satisfied the return the answer,2.5
-4390,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",t(n)=t(n-1)+c(n),0.0
-4391,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",the function which calls itself is called as recursive function ,2.5
-4392,What is a recursive function?,"A recursive function is one which calls itself, directly or calls a function that in turn calls it. Every recursive function follows the recursive properties − base criteria where functions stops calling itself and progressive approach where the functions tries to meet the base criteria in each iteration.",recursive function is a function where we call the one part of the code after sometime or recursively one and after.,0.0
-4393,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4394,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique we take the lower bound and upper bound of the elements and upper-lower=height of the elements. a formula is already declared for interpolation search which is hi+(hi-lo)/2+li we execute this and search either in the first half of array or in the next half,2.5
-4395,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4396,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is a modified binary search, but instead of taking the middle element for division it uses a formula for better and efficient solution",2.5
-4397,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is a searching method that is used to find a given element on the basis of a formula that calculates the approximate location of the element inside the array. It then checks whether the element is present at that location. If not then it checks the left and right of that index depending on the element to find else it returns the index.,2.5
-4398,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,pos=x-(arr[high]-arr[low])/high-low\nand then we first check that elemnt if its is smaller than arr[pos] then we will check in left part or otherwise in right subpart of the pos element.,2.5
-4399,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4400,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4401,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4402,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4403,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"It is a search technique which is used to search a element, basically it is a formula that gives the index value of the searched elememt.",2.0
-4404,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search technique that is used to search the element in an array , it gives the position of the array where the element can be present using the formula.",2.5
-4405,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a searching techniques which compare every key with the given element to be searched.,2.5
-4406,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4407,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique has a time complexity of O(log n ).,2.5
-4408,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,We use this technique when array is sorted and their is a uniform gap so that we can compute the index of required element.\nind = low + (arr[high] - key)*arr[low]/(high - low),2.5
-4409,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is a modified version of and similar to binary search that inputs an increasingly sorted array, applies its formula and tries to find the desired element",2.0
-4410,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"A recursive function is a function which needs to be called once and then it calls itself again and again with the proceeded values until the base condition become true.\nOnce the base/ground condition hits, all the recursive functions will start retuning the values and start coming back to the previous function one by one and in this way it will come out of all the recursive calls and return the final value.",2.5
-4411,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is searching technique in which maximum and minimum elements of the array are defined and then used to compute sorting.,2.5
-4412,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search technique is a searching technique where the elements are searched one by one using interpolation.,2.0
-4413,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,A heap data structure is similar to a tree that it has a root and the nodes. It is arranged according to max heap or min heap way.,2.0
-4414,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys.,2.5
-4415,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4416,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"When a particular function calls itself repeatedly, it is called recursion. recursion is an easy way to solve larger problems and is extensively used in various approaches like divide and conquer. However, one drawback of recursion is that it takes a lot of time to implement. ",2.5
-4417,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search a best technique to search an element in o(n) time  ,2.5
-4418,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,The element is searched on basis of ASCII values.,2.0
-4419,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4420,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is used for searching an element in an array with a formula in log (n) time complexity.,2.5
-4421,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4422,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is similar to binary search where the array is divided into 2 halves and formula is used to find the next element to be compared.,1.0
-4423,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4424,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,we pick an element and form that index we divide the given array in two parts and we placed all smaller element in left side and larger element in right side. and we search the particular index for a given element.,2.5
-4425,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is advanced version of binary search technique.\nsearching is done using a formulae.,2.5
-4426,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4427,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a type of binary search technique. \nThis technique can be solved by using a particular formulae.,2.0
-4428,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation Search is a technique obtained by enhancing binary search technique. Unlike binary search, in interpolation search we use a sorted array which is uniformly distributed.",2.0
-4429,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a search technique that uses a formula to find an element with which key element is compared and then recursively this function is re-called to find the key element.,2.5
-4430,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4431,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"A heap is a way to form to represent data. A heap has its own functions like heapify , heapsort to display and sort the data.",1.0
-4432,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is a more statistical approach to binary search in which instead of soughting after the mid element, we choose the element on the basis of a formula.",2.5
-4433,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In interpolation technique, we choose an element using a formula and then divide the array into two subarrays of index 0 to x and x+1 to n and the recur to follow the same process until the element being search is found.",2.5
-4434,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In interpolation technique we choose an element using a formula say x, and then divide the array into two subarrays of index 0 to x and x+1 to n and then recur to the first subarray if element to be found is smaller than x else recur to the other subarray and  follow the same process until the element been searched for is found.",2.5
-4435,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,in interpolation search technique you search a particular required desired value in the given data structure.,2.5
-4436,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,A function which is called several times is known as a recursive function.\nRather than defining the function again and again it is just called several times with required parameters.,2.5
-4437,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In this search technique, the elements which are smaller than the pivot element are shifted to the left of the array while the elements larger than the pivot are shifted to the right side of the pivot.",1.0
-4438,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,In interpolation search technique we use the concept similar to binary search difference being that we do not pick the middle element but assign  G variable which can be any of the element from the given data. The time complexity remains same as logn.,1.0
-4439,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation Search is similar to Binary Search technique., the difference is that in interpolation search the element by which all other elements are compared is calculated by a formulae instead of choosing middle element like in binary search.",2.5
-4440,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Somewhat similar to binary searching technique, interpolation search technique has only one difference that the element to searched is found using a given formula.",2.5
-4441,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Somewhat similar to binary search, interpolation search technique ",2.0
-4442,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is somewhat similar to binary search.,2.0
-4443,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is a type of technique used to search data from the stored values. It uses a formula as (arr[hi]-arr[lo])(arr[lo])/(hi-lo).,2.5
-4444,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a technique in which we determine the approximate position of the element from array and search for that element on that position,2.5
-4445,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"\n\nInterpolation search is a search algorithm used to find the position of a target value within a sorted array of values. It works by using a linear interpolation to estimate the position of the target value within the array, then narrowing the search range iteratively until the target value is found or determined to be not present in the array. This technique is particularly useful when the values in the array are uniformly distributed, allowing for a more accurate estimate of the target value's position.",2.5
-4446,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"interpolation search technique is a searching algorithm which involves using a formula to calculate the approximate value for supposed position of the element to be searched for, it works better when elements are spaced more equally.",2.5
-4447,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is used to search the element that other searching techniques cant find easily. It has a formula that can be used to find elements by interpolation or extending the list. ,2.5
-4448,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"It is a search technique similar to binary search. But in this ,instead of the middle element , we choose the index using a specified formula.",2.0
-4449,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is a kind of searching technique which is like binary search but is more optimized n nature,2.0
-4450,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search helps you to search an element from the array. ,1.0
-4451,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is used for sorted array to search for a key,2.5
-4452,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is a technique in which searching done using polynomial equation.,2.5
-4453,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,search key in array ordered by numerical values,2.5
-4454,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is an improvement of binary search to numeric vales assigned by keys,1.0
-4455,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,when collision occur then its called interpolation  ,2.5
-4456,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a modified version of binary search,2.5
-4457,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"it needs sorted and equally distributed collection of dat, it works on probing position of the required value.",1.0
-4458,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is seaching in multiple groups at the same time ,2.5
-4459,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,An interpolation search technique is a technique of searching the information bidirectionally.,1.0
-4460,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys. in the searching is done in less complexity to the linear searvh.,2.5
-4461,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it a better version of binary search where values are distributed equally in a sorted array ,2.5
-4462,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,The interpolation search techniques is a modified version of quick sort and is best suited for sorted arrays. It uses a formula to find the hinge element and then sorts the array using the formula.,2.5
-4463,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search technique is a search technique in which a pivot is selected using a formula and three arrays are created,  one of elements smaller than pivot, one with the pivot and one of elements larger than the pivot. ",2.5
-4464,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is an searching technique to search for keys that are ordered in the values given to them,2.0
-4465,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is a type of search technique like binary search which uses divide and conquer technique.,2.5
-4466,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is a type by which people search a telephone number for a name. The Interpolation Search is an improvement over the binary search for instances, where the values in a sorted array are uniformly distributed. Binary Search always goes to the middle element to check. while interpolation search always looks out for the links.",2.5
-4467,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4468,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Insterpolation search is nothing but a special binary seach tree with a formula,2.5
-4469,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique finds of the value of the required data with the help of the help of the given data and the database.,2.5
-4470,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,in interpolation search technique we divide the array by finding the value of a variable by dividing the array according to the weight of the elements by its size and then finding the element by finding its relative position according to the weight it carries.,2.5
-4471,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys. It is basically improvement of binary search.\n\n,2.5
-4472,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search allows us to ,2.5
-4473,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,in this we use the formula for finding the element for any case. and this is the improvement of binary search ,2.5
-4474,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search Technique is an enhanced version of the binary search technique,1.0
-4475,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation is a search technique with O(1) time complexity.It is like binary search bu7t better it searches the required answer from the formula.the formula calculated the index and gives the answer.,2.5
-4476,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation searching technique is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys.,1.0
-4477,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is the extended version of binary search where lowest and highest index id generally used through a formula to search a element,2.5
-4478,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation is a searching technique that uses a formula which intakes indexes and array and returns the index of the required element.It is very quick and takes O(1) COMPLEXITY,2.5
-4479,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is used in case of sorted data.It uses a pivot element and has approach similar to median search.,2.5
-4480,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is based on the formula where we find the left most and right most number in the array and place them in the formula to find out the output.,2.5
-4481,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,INTERPOLATION Search is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys. ,2.5
-4482,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is an improved variant of binary search work on probing position of required value. ,2.5
-4483,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"in interpolation search technique , we search the given element using given function.",2.5
-4484,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a better version of Binary Search as it reduces the time complexity,2.0
-4485,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is a formula based search technique.,1.0
-4486,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"its an improved version of binary search ,it searches  a key that has been ordered by numeric values assigned to the key.",2.0
-4487,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4488,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a search technique to find the Kth smallest or largest element in an array.\n,2.5
-4489,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is an improvement of a binary search where the values are uniformly distributed and works with a formula,2.5
-4490,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,A recursive function is a function that calls itself. ,2.5
-4491,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Recursive functions are functions which are used to call itself recursively untill the base condition is true.,2.5
-4492,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4493,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,where parts of the array are taken and make the smaller one at the first and comparing it with other elements,1.0
-4494,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search is advance form of search in which we have formula to calculate the search space and get the element,2.0
-4495,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,
-4496,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is an improved varient of binary seacrh. This seach works on the probing position of the required value.,1.0
-4497,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,
-4498,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it  is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys ,1.0
-4499,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Median Search, we use pivot to search. Then the key is compared with pivot. Array before pivot is taken if key < pivot. If its greater array after pivot is taken and further processed. ",2.0
-4500,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search technique uses a particular formula to calculate the middle element in the array and then uses recursion to solve the problem in different parts, first recursion works from 0th element to the middle element and second recursive call works from (middle+1)th element to the last element.",1.0
-4501,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"interpolation search technique is same as median search, only there is a different formula for element picking.",2.0
-4502,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is an modified version of binary search which has the formula of mid element that consists of start, end and key unlike binary search that has mid formula consisting of only start and end in it.",1.0
-4503,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search technique uses a formula to find out the answer. It calculates the index from last. ,1.0
-4504,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is used to find an  element in a sorted array.it is an improvement over binary search.it makes the guess of the position of the target element by the formula that takes first and last element and the key in it.,1.0
-4505,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search technique is the main algorithm which is utilized by the median search in finding out the desired value using a primarily calculated pivot point.,2.0
-4506,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,A function in which the function is calling itself is called a recursive function,2.0
-4507,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search looks for a very particular value of pivot and can be used in median search. Earlier we used to choose random values. More efficient that normal median search.,2.0
-4508,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,This is an enhanced form for binary seach where the element is not divided exactly from the middle but an improved probability element is taken by the formula.this technique is called interpolation search technique.,1.0
-4509,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,The Interpolation Search is an improvement over Binary Search for situation in which where the values in a sorted array are uniformly distributed.,2.0
-4510,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Recursive function is a function which calls itself inside its own function body .,2.0
-4511,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is an advanced form of binary search where instead of dividing the array from the middle we divide the array from the element whose value is close to the value we are looking for.,1.0
-4512,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of the required value. For this algorithm to work properly, the data collection should be in a sorted form and equally distributed.",1.0
-4513,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In interpolation search, we define a searching formula, store it in a variable, let's say x and then we compare the target element with x, whether it's greater or smaller than x. If it is equal to x then we directly return it but if not then we change our start and end index.",1.0
-4514,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search ,1.0
-4515,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is used in median search\n it takes very precise value of the pivot\nit is more efficient,1.0
-4516,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,The method in which the element is searched by dividing the array into two parts on the basis of power of 2.,1.0
-4517,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"n(n-2)  ,where n is number of vertices",1.0
-4518,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,
-4519,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is for searching for a key in an array that has been ordered by numerical values assigned to the keys.,2.0
-4520,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a more advanced and upgraded version of binary search in which we instead dividing array from the middle try to divide it in such a way that the element chosen to divide is close to the element we are searching with the help of a formula.,2.0
-4521,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value., In this technique we divide the find a point use formula   low+(arr[high]-arr[low])+1/(high-low).Then check the value is greater than or less than then according that recursive function is called.,1.0
-4522,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation Search is a divide and conquer searching algo in which an element (x) is searched in a sorted array.\nAn element (y) is chosen then if x > y then the array(y,end) is chosen and if x < y then the array (start, y-1) is chosen. This is repeated till x is not found.",2.0
-4523,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,A recursive function is the function in which the function calls itself in the definition of the current function,1.0
-4524,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"interpolation search technique refers to finding the elements form middle of the array or string by calculating the size od array and finding key , as we find the key we start to search from their ",1.0
-4525,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation is a searching technique used by applying recursion and it divides the problem in parts to the smallest elements and then compares them each by each\nuntil the desried element is found.\n,2.0
-4526,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,binary search implementation search ,2.0
-4527,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a searching a key in an array ,1.0
-4528,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is a lot like median search,2.0
-4529,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a search technique which helps to find the maximum or minimum element in the array .with less time .it uses a formula which helps to find the element ,2.0
-4530,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is a technique which is same like median search in this technique we assume a formulae that gives the nearest neighbors of the elements to be searched ,1.0
-4531,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is a type of modified median search.,2.0
-4532,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,
-4533,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"a recursive function is made to solve a big problem by breaking it into smaller parts thus it consist of a base condition,  the main operation that has to be performed on the smaller problem and then another call to the same function(recursive call).",2.0
-4534,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,searching through a formula for middle element.,2.0
-4535,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"interpolation  search technique is applied on sorted array .  as we do in  median search using middle index we find the index by averaging out the array , then applying interpolation search on right part if the element we are searching is bigger than the element at that index or left if smaller and return the index if the value matches . ",1.0
-4536,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is the one in which we have a formula to find the most probable index where the search result can be present. It works a bit like the binary search but instead of going to the middle always it uses a formula. ,1.0
-4537,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation is an improvement over binary search where values in a sorted array are uniformly distributed. It created new data points within the range of a discrete set of known data points.,1.0
-4538,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is the version of binary search in which we use a different formula to search any element.,2.0
-4539,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Modification of binary search. we use a different formula to find the mid or the point from where we have to divide the array.,1.0
-4540,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Function calling itself is what called a recursive function.,1.0
-4541,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a modification of the binary search technique which does not aim for the middle element and starts it comparison but instead calculates its own element which is greater than the smallest one and greater than the larger one and goes on finding that approximation index and searches the element accordingly.,2.0
-4542,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique is similar to binary search where we first finds the middle element and then searches in the half half parts of the array. ,1.0
-4543,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In this search technique, we find middle element from array based on an expression and then divide the array in two parts one from first element till middle and second from middle to last element and try to find our element using recursion.",1.0
-4544,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is somewhat similar to binary search just the difference is that instead of taking mid we take another value using a formula.,1.0
-4545,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is a search that uses a certain formula to find a kind of partition then ,0.0
-4546,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation seach technique is a divide and conquer algorithm, which is the modified version of binary search which provide a formula to reach the correct location of the element fastly.",1.0
-4547,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,in interpolation search,1.0
-4548,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search technique helps in finding the desired elements by a specific formula\n//index i = x(arr[hi]-arr[lo])/hi-lo,1.0
-4549,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"it is a technique to find an element in which works in a sorted array, find the median then work accordingly , on the basis of it moves accordingly left or right.",1.0
-4550,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is a searching technique in,0.0
-4551,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"A recursive function is a function which calls itself again and again until the base condition is reached, then it terminates.",1.0
-4552,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search technique searches using the formula of sum of a arithmetic progression.,1.0
-4553,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is an improved alternative of binary search where value is stored in a sorted array in a sorted manner.,1.0
-4554,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Based on divide and conquer. Randomly divide and pick a subpart and search for the key. We take the middle element and then recursively search the other halves. ,1.0
-4555,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Follows divide and conquer technique.\nIt uses the formula pos=(h-l)*(a[h]-a[l])\nwher h =last index, l = first index",1.0
-4556,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is like median search ,0.0
-4557,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,This technique uses divide and conquer approach  where the values are in a sorted array are uniformly distributed. Interpolation constructs new data points within the range of a discrete set of known data points. ,1.0
-4558,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,the interpolation is type of median search,0.0
-4559,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,in interpolation search technique we use the formula of high and low by searching the key for an array .,1.0
-4560,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4561,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation is search technique to find search the data in,1.0
-4562,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4563,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is algorithm for searching for a key in an array that is of numerical values.,1.0
-4564,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is use to searching a key in an array that has been ordered by numerical value assigned to the keys.,1.0
-4565,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In a interpolation search technique, it uses an already sorted array to search an element in an array with the help of its formula.\nIt is similar to binary search technique where we first compare the element ",1.0
-4566,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is similar to binary search but more accurate rather than verifying the middle element we find a better pos ,1.0
-4567,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,In the interpolation search technique it uses an already sorted array of integers to find a particular element in an array.\nIt uses a formula and does a binary search on the array. ,1.0
-4568,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"We use this technique to search for an element in already sorted array, it uses a predefined formula. ",1.0
-4569,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4570,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"The Interpolation Search is an improvement over Binary Search for instances, where the values in a sorted array are uniformly distributed.",1.0
-4571,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is a search which starts from the middle element of the array and searches for the element by everytime taking the middle  of  array starting fom the firstly selected middle element to its left  or right accordingly,1.0
-4572,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is search technique and to apply it elements of array need to be sorted. It follows a general formula which find out the index to be checked by using middle element and range of array --> ind = mid - (arr[high] - arr[low])/(high-low)*element to be searched.,1.0
-4573,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4574,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,uniform type search.,1.0
-4575,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"The Interpolation Search is an improvement over Binary Search for instances, where the values in a sorted array are uniformly distributed. Interpolation constructs new data points within the range of a discrete set of known data points. Binary Search always goes to the middle element to check. On the other hand, interpolation search may go to different locations according to the value of the key being searched. For example, if the value of the key is closer to the last element, interpolation search is likely to start search toward the end side.\n",1.0
-4576,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,when data is distributed uniformly we apply interpolation search\nit relies on mathematical formula to search for an element in the array,1.0
-4577,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is the search technique that search the data in constant time but only in one condition that data is uniformly distributed ,1.0
-4578,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is improved version of binary search it works on probining and postion of the required value,1.0
-4579,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Heap is a special type of data structure which functions in the way of tree as a method to balance or sort the elements present in any data structure. There are two types of sub heaps that can be implemented i.e. min heap and max heap. Min heap keeps the minimum element on top, and max element does vice versa",1.0
-4580,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys .,1.0
-4581,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,When we have the available data in the uniform pattern. And we search for the data at an index according to the specially devised formula which is something like the binary search but instead of index we find the half of the values at the two ends of the problem space that we are analyzing. Then we further reduce the problem space until we either find the data or declare that its not present. \nIn the best solution it can give the answer in const time only.,1.0
-4582,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a technique in which we determine the appropriate position of the element from the array  and search for that element on that position.,1.0
-4583,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is a searching method for sorted array/linked list which use a formula to find specific element (Generated through research) which needed to be check if it is the required element or not ,if not then again generate element using that formula till the element is found else return not found.",1.0
-4584,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a searching technique wherein ,1.0
-4585,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a type of searching technique in which the element is searched on the basis of certain formula.,1.0
-4586,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys.,1.0
-4587,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is a searching technique in which searching of the element is done using a specific formula ,1.0
-4588,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4589,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In a heap elements are arranged in the form of a binary tree by 2 methods - min heap , max heap. In min heap the minimum element is placed at the parents node. In max heap the maximum element is placed at parent node.",1.0
-4590,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it works on divide and conquer approach . it is similar to binary search but instead of find the middle element we find the element by using the formula .,1.0
-4591,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,It is a type of search technique in which we find an element expected position according to a formula and then divide the elements in three parts . One contains elements less than other contains that same element and last contains elements greater than. This is performed recursively and solution is obtained. In median search we take middle element for this purpose.,1.0
-4592,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"it is a type of searching performed on a sorted data/array in which we want to find K th largest/ K th smallest element ,i.e. element at a particular position using a special formula.",0.0
-4593,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4594,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4595,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search involves a target value k it has a formula whose idea is to,0.0
-4596,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is a variation of binary search where the index to be searched is given by a certain formula,2.5
-4597,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,A searching technique where we hook on a middle element and move the iterator in right and left direction.,0.0
-4598,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4599,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search involves a target value K .It has a formula whose idea is to return higher value of position .,2.0
-4600,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4601,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4602,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation search searches for the element in the middle then looks for the element to its left or right depending upon weather its smaller or larger to given element.,2.0
-4603,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4604,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,in this search we use arithmatic formulas to calculate the mid element and then compare the other elements with it recursively,1.0
-4605,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it is a extension to binary search technique in the index to replace mid  is calculated by a given formula,2.5
-4606,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4607,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,Interpolation Search technique algorithm utilizes the idea of Arithmetic Progression to search a particular element . While using IP search it is considered that consecutive elements have a constant or defined common difference. It makes search a lot easier and we can get the result of search in 2 to 3 search.,2.5
-4608,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4609,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"It is like binary seach, but makes use of mathematical formulas to improve the functioning of binary seach.",2.5
-4610,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is an  inplace searching algo,0.0
-4611,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4612,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4613,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In Interpolation search , we use aithmetic progression formulae to calculate the mid element . We first find the mid element using that formulae and then compare the element to be searched whether it lies on the right side or left side of array.",2.5
-4614,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4615,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is defined as the median search problem  by creating the comparison.,0.0
-4616,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4617,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"In this search technique, poles of the data structures are decided and the desired element or pattern is searched accordingly.",0.0
-4618,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4619,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4620,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4621,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,it reffers to the merge sort which follows divide an conquer approch,0.0
-4622,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4623,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search is search is,0.0
-4624,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,median search technique used,0.0
-4625,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4626,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"Interpolation search is applied on sorted arrays. Interpolation search technique is a method to find an element via a formula, then if the target element is less than the element selected then we choose the left part of the array assuming the array is sorted in increasing order, else we choose to search in the right part of the array and the process continues on until the search space becomes null or we find the target element.",2.5
-4627,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4628,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,"it is median search technique, in which we divide array into two equal halves and look for the element seperately in two different halves , basically reducing time.",1.0
-4629,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4630,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,in interpolation search the values are first traversed and stored in a table and their occurrence is also stored beside them.\n and then sorted as per the number's that are repeated.,0.0
-4631,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is work on the database to search a function in the potation manner ,0.0
-4632,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,searching technique in which parts of the data structures are pre decided by the user earlier.,0.0
-4633,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,interpolation search technique is ,0.0
-4634,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,find out polar form.,0.0
-4635,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,na ,0.0
-4636,What is interpolation search technique?,Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of required value.,,0.0
-4637,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4638,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix-   ab+cd+*\nprefix-    *+ab+cd\n,0.0
-4639,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4640,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: *+ab+cd\npostfix: ab+cd+*,2.5
-4641,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix:   ab+cd+*\nprefix:    *+ab+cd,0.0
-4642,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Postfix: ab+cd+*\nPrefix:*+ab+cd,0.0
-4643,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4644,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix - *+ab+cd\npostfix- ab+cd+*,2.5
-4645,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix-    *+ab+cd\npostfix-   ab+cd+*,2.5
-4646,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4647,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix-    *+ab+cd\npostfix-    ab+cd+*,2.5
-4648,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix :-  *+ ab + cd\npostfix :-  ( a b + ) * ( c d +),2.5
-4649,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"Prefix:- (,),+,*\nPostfix:- *,+,(,)",0.0
-4650,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,pre *(+ab)(+cd)\npost (cd+)(ab+)*,2.5
-4651,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix : *+ab+cd\npostfix : ab+cd+*,2.5
-4652,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix : ab+cd+*\nPostfix : +ab*cd+,0.0
-4653,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix:   *+ab+cd\nPostfix:  ab+cd+*,2.5
-4654,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,This is a new kind of search technique in which an increasingly input data is entered ,0.0
-4655,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*+ab+cd \nab+cd+*,2.5
-4656,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4657,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,In a recursive function we repeat the recursive function multiple times according to the instruction written and we move backwards forming a recursive tree to get our result.\nIt has a base condition so that by moving backwards we get a way to solve the problem by moving forward once more,0.0
-4658,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4659,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation: *\npostfix notation: +,0.0
-4660,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,interpolation search technique is a search technique which uses ,0.0
-4661,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix : (ab)(cd)++* or  (ab)(cd)*++\nprefix : *++(ab)(cd) or ++*(ab)(cd),0.0
-4662,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix is ab(+)*cd(+)\npostfix ab(+)*cd(+),0.0
-4663,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: ab(+)*cd(+)\npostfix: ,0.0
-4664,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix : ab+*cd+\npostfix : ,0.0
-4665,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4666,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix: *+ab+cd\nPostfix: ab+cd++*,2.5
-4667,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4668,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4669,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4670,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4671,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4672,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4673,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4674,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefic ac postfix ad.,0.0
-4675,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,A function is a recursive function if it calls itself in the function.,0.0
-4676,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: *+ab+cd\npostfix: dc+ba+*,2.5
-4677,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix:  *+ab+cd\nPostfix:  dc+ba+*,2.5
-4678,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*+ab+cd - prefix\ndc+ba+* - postfix,2.5
-4679,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*ab+cd=prefix\ndc+ba*,2.5
-4680,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4681,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix:- (a+b) * (c+d)\nPostfix:- (b*c)+a+d,0.0
-4682,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix: *+abcd\nPostfix: abcd*+,0.0
-4683,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Postfix : *+ab + cd\nPrefix : ab + cd+*,2.5
-4684,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix: ab + cd+*\nPostfix:  *+ab + cd,0.0
-4685,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: ab+cd+*\npostfix: *+ab+cd,0.0
-4686,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix= ab+cd+*()\nPostfix= *+ab+cd,0.0
-4687,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix Notation: ab+cd+*\nPostfix: +ab*+cd,0.0
-4688,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,POSTFIX - ab+cd+*\n\nPREFIX - *+ab+cd,0.0
-4689,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,\n\nThe prefix notation of the expression (a + b) * (c + d) is: * + a b + c d\n\nThe postfix notation of the expression (a + b) * (c + d) is: a b + c d + *,0.0
-4690,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix is (*+ab+cd)\nPostfix is (ab+cd+*),2.5
-4691,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix -> *+ab+cd\npostfix -> ab+cd+*,2.5
-4692,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,(ab)(cd)*++,0.0
-4693,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix- ac\npostfix- bd,0.0
-4694,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix - ab+cd+*\nPostfix - *+ab+cd,0.0
-4695,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix notation:  * + a b + c d\nPostfix notation:  a b + c d + *,0.0
-4696,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix : * + a b + c d\nPostfix : a b + c d + *,2.5
-4697,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,-,0.0
-4698,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,+\n*,0.0
-4699,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix-(a+b)\npostfix-(c+d),0.0
-4700,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4701,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,PREFIX NOTATION--AC\nPOST FIX NOTATION-- BD,0.0
-4702,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4703,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4704,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,((A + (B * C)) + D),0.0
-4705,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4706,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4707,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4708,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4709,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix notation :  ab+*cd*\n\nPostfix notation :\n,0.0
-4710,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix- *+ab+cd\nPostfix- ab+cd+*,2.5
-4711,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation -++*abcd\npostfix notation - abcd*++ ,0.0
-4712,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: abcd*+()\npostfix: ()+*dcba,0.0
-4713,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,the prefix value is a+b while the post fix value is c and d.,0.0
-4714,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix form *+ab+cd\npostfix form  ab+cd+*,2.5
-4715,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4716,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4717,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4718,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Postfix - a(b+*)c(d+)\nPrefix - ()*ab()cd,0.0
-4719,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix=*+ab+cd\npostfix=ab+cd+*,2.5
-4720,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4721,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4722,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix -ab postfix-ab ,0.0
-4723,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4724,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix- *(+ab)(+cd)\npostfix- (ab+)(cd+)*,2.5
-4725,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4726,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4727,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation= ac+bc\npostfix notation= bc+bd,0.0
-4728,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Postfix - ab+cd+*\nPrefix- *+ab+cd,0.0
-4729,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4730,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,pre : *+ab+cd       post: ab+cd+*,2.5
-4731,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4732,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix notation: +*+abcd\nPostfix notation: abcd+*+,0.0
-4733,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4734,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4735,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,In Interpolation search first we find the position pos then we check if element lies to the left or right of the position if it is in the left side then we do search till the left half and if it lies in right then we do the search the right half untill we get the element equal to the arr[pos].,0.0
-4736,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*(+ab)(+cd)\n(ab+)(cd+)*,2.5
-4737,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"a(c+d), b(c+d)",1.0
-4738,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,(a+c)*(b+d),1.0
-4739,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4740,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix notations are : ++a*bcd\nPostfix notations are : abc*+d+,1.0
-4741,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*(+ab)(+cd)-prefix\n(ab+)(cd+)* - post fix,1.0
-4742,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *, prefix == + + A * B C D\npostfix == \tA B C * + D +,2.0
-4743,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4744,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4745,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*(+ab)(+cd)(ab+)(cd+)*,1.0
-4746,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4747,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: *(+ab)(+cd)\npostfix: (ab+)(cd+)*,2.0
-4748,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4749,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4750,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Interpolation search is a form of binary search technique in which dont calculate mid instead we divide the array at some other index.,1.0
-4751,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix - *+ab+cd\npostfix - ab+cd+*,2.0
-4752,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix  :     + + A * B C D    \nPost Fix:\tA B C * + D +,2.0
-4753,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix-   * + ab + cd\t\npostfix-   ab +cd + *,1.0
-4754,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Interpolation search is an algorithm for searching for a particular  key in an array that has been ordered by numerical values assigned to the keys.,2.0
-4755,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix: *+ab+cd\nPostfix: ab+cd+*,1.0
-4756,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"The expression a + b * c + d can be rewritten as ((a + (b * c)) + d) to show that the multiplication happens first, followed by the leftmost addition. a + b + c + d can be written as (((a + b) + c) + d) since the addition operations associate from left to right",2.0
-4757,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4758,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix notation : * +a b + c d\nPostfix notation : a b + c d + *,1.0
-4759,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4760,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix-*((+ab)(+cd))\nPostfix-((ab+)(cd+))*,1.0
-4761,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,heap is a binary tree which is balanced by compairing the root node key with it's childrens and arranged accordingly.,2.0
-4762,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix Notation : *(+ab)(+cd)\nPostfix Notation : (ab+)(cd+)*,1.0
-4763,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4764,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation: *+ab+cd\npostfix notation: ab+cd+*,2.0
-4765,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4766,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix :- *+ab+cd\nPostfix :- ab+cd+*,2.0
-4767,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"In Interpolation search, we need a sorted array and the element is searched using the function such as - (i=2*i) if the value at 'i' is greater than the value to be searched, then the element is searched in that range using binary or linear search.",1.0
-4768,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4769,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix : ab+cd*+\npostfix: *ab+cd+,2.0
-4770,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"prefix = a,b\npost fix = c,d",2.0
-4771,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix is * and post fix is +,1.0
-4772,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*(+ab)(+cd)\n(ab+)(cd+)*,2.0
-4773,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix a\npostfix d,2.0
-4774,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix - : *+ab+cd\npostfix- ab + cd +*,1.0
-4775,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,post fix:   ab+cd+*\nprefix:      *+ab+cd,2.0
-4776,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,+ab*+cd prefix notation\nab+cd+* postfix notation,2.0
-4777,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,interpolation search technique is almost similar to binary search but has definite formula to find the middle element.,1.0
-4778,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix=ab+cd+*\nprefix=*+ab+cd,1.0
-4779,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"prefix :- *+ab+cd , postfix: - ab+cd+* ",2.0
-4780,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*+ab+cd\nab+*cd+\n,2.0
-4781,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4782,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix - *  +ab +cd\nPostfix-  ab+ cd+ *,2.0
-4783,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix : ++*abcd\npostfix : ab*cd++,2.0
-4784,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,
-4785,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix :  *+ab+cd\npostfix : ab+cd+*,2.0
-4786,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix - ,0.0
-4787,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix: *+ab+cd\nPostfix: ab*+cd+,2.0
-4788,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: *+ab+cd\npostfix: ab+cd+*,2.0
-4789,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix (+ab)(+cd)\npostfix ab+*cd+,2.0
-4790,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix-\n+ab*+cd\nPostfix-\nab+*cd+,2.0
-4791,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix = abcd+*+()()\npostfix = ()()+*+abcd,2.0
-4792,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Postfix: ab+c*d+\nPrefix: +ab*c+d,2.0
-4793,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: ++*abcd                                    postfix: a*b+cd,2.0
-4794,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix= ab+cd+*\nprefix=*ab+cd,2.0
-4795,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4796,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix- ab+cd+*\nprefix- *+ab+cd,2.0
-4797,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4798,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix -     *(+ab)(+cd)\nPostfix-     (ab+)(cd+)*,2.0
-4799,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix = *+ab+cd\nPostfix = ab+cd+*,2.0
-4800,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*(+ab)(+cd)(ab+)(cd+)*,2.0
-4801,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: *ab+cd\npostfix: ab+cd+*,2.0
-4802,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*(+ab)(+cd)\n(ab+)(cd+)*,2.0
-4803,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *, prefix: + + a * b c d  post fix: a b c * + d +,2.0
-4804,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix: *+ab+cd\npostfix: ab+ccd+*,2.0
-4805,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prfix:a+b\npost fix:*c+d,2.0
-4806,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notations :- \,0.0
-4807,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation :    * + A B + C D\npost fix notation :   A B + C D + *,0.0
-4808,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4809,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4810,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4811,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4812,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix- (ab)+(cd)+*,0.0
-4813,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,a+b*c+*d,0.0
-4814,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"int a,b,c,d;\ncout<<((a+b)*(c+d))<<endl;",0.0
-4815,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4816,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix -> +*ab+cd\npostfix -> ab+*cd+,2.0
-4817,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix - *ab+cd;\nPostfix - ab*cd;,2.0
-4818,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*+a b c prefix\n + + a b c post,2.0
-4819,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"The expression A + B * C + D can be rewritten as ((A + (B * C)) + D) to show that the multiplication happens first, followed by the leftmost addition. A + B + C + D can be written as (((A + B) + C) + D) since the addition operations associate from left to right.\n",0.0
-4820,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation-> * +ab +cd\npostfix notation-> ab+ cd+ *,0.0
-4821,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,ab+cd+*\n*+cd+ab,2.0
-4822,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,*+ab+cd\nab+cd+*,2.0
-4823,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"recursive function is a function calling itself, it usually also has a base case to get out of the recursive loop",0.0
-4824,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,\tPrefix notation: * + A B + C D and\tPostfix notation: A B + C D + *,0.0
-4825,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Post-Fix\nab+cd+*\n\nPre-Fix\n*+ab+cd,0.0
-4826,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix : *+abcd\nPostFix : ab+cd*,2.0
-4827,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix:  *()+ab()+cd\npostfix: ab+()cd+()*,2.0
-4828,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Postfix:\nPrefix:,0.0
-4829,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix= *()+ab()+cd\npostfix= ab+()cd+()*,1.0
-4830,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4831,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix-> *+ab+cd\npostfix->ab+cd+*,2.0
-4832,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix=ab+()cd+()*\nprefix=*()+ab()+cd,1.0
-4833,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,In recursion the same function is called inside a function with different arguments. \n\nsteps involved to write a recursive function-\n1. base case\n2. n-1 th case - obtained from principle of mathematical induction\n3. return solution,0.0
-4834,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix->+ab*+cd\npostfix->ab+cd+*,1.0
-4835,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix  +ab*+cd\npostfix  ab+cb+*,1.0
-4836,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix-*a+*b\npost,0.0
-4837,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix a+b,0.0
-4838,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix  *(+ab)(+cd)\npostfix  (ab+)(cd+),2.0
-4839,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notating is this (a + b) * (c + d) \npostfix notation is (ac + ad + bc + bd),0.0
-4840,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix- *+ab+cd\npostfix- ab+cd+*,2.5
-4841,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4842,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix: (ab+)(cd+)*\nprefix: *(+ab)(+cd),2.5
-4843,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4844,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation of (a+b)*(c+_d)\nis dc*ba++\npostfix notation of (a+b)*(c+d)\nis ab*cd++,0.0
-4845,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,+*(ab)(cd) prefix\n(a+b)(c+d)* postfix,2.5
-4846,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix:  *+ab+cd\n\npostfix: ab+cd+*,2.5
-4847,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4848,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4849,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix is ab+cd+*\nprefix is *+ab+cd,2.5
-4850,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4851,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix: dc*ba++\nPostfix: ,0.0
-4852,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4853,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4854,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,ab+cd+*,0.0
-4855,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4856,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix= (*)\npostfix=(+),0.0
-4857,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix ;- *+ab+cd\npostfix :- abcd==*,0.0
-4858,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4859,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix (a +b)\n(a +b)*\n(a +b)*(c +d),0.0
-4860,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4861,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix: *+ab+cd\n\nPostfix: ab+cd+*,0.0
-4862,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *, pre fix => ac + ad\npost fix => bc + bd,0.0
-4863,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation:- ac + ad\npost fix notation:- bc + bd,0.0
-4864,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,postfix notation = ab+cd+*\nprefix notation = ++*abcd,1.0
-4865,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,ac + ad,0.0
-4866,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation: ac + ad\npost fix notation: bc + bd,0.0
-4867,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix *+ab+cd\npostfix ab+cd+*,2.5
-4868,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix *+ab+cd\npostfix ab+cd+*,2.5
-4869,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix notation :-  a\nPostfix notation :- d,0.0
-4870,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,Prefix Notation: *(+ab)(+cd)\nPostfix Notation: (ab+)(cd+)*,2.5
-4871,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,,0.0
-4872,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix:   +*ab+cd\npostfix : ab+cd+*,1.0
-4873,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix is a and postfix is d,0.0
-4874,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix -> (a+b).(c+d)\npostfix -> (a+b) + (c+d),0.0
-4875,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix a+b+c+d\npost fix (ab)*(cd),0.0
-4876,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix - *+ab+cd\n\npostfix - ab+cd+*,2.5
-4877,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,"prefix is a+b ,c+d\npost fix  sum to both",0.0
-4878,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,a,0.0
-4879,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix a and postfix d ,0.0
-4880,What is the prefix and post fix notation of (a + b) * (c + d) ?,(1)Prefix Notation − * + a b + c d (2)Postfix Notation − a b + c d + *,prefix notation=(a+b) (c+d)*\npost fix notation= * (a+b)(c+d),0.0
-4881,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4882,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it stores the element in a sequence pattern\nit stores  element in a sorted manner\nit assign a address to the element where it has stored,1.0
-4883,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,its features are public and predefined funciton ,1.0
-4884,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT stands for Abstract data type. It can create an abstract class but it cannot be used to create an object of the class. ,1.0
-4885,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Abstract Data Type allows data structure to be implemented without actually showcasing the working of the data structure.,1.0
-4886,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT is a abstract data type.,1.0
-4887,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it exports a type .\nit export set of operation called interface.\nADT is reusable.\n,2.0
-4888,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,.it is reusable.,2.0
-4889,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4890,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4891,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4892,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"It helps to store the memory of the RAM as we make the array dynamically  and we can delete the rest over array if not in use, whereas in a normally array it acquire the size and if there is empty array the size will remain occupied there .",2.0
-4893,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"The features of ADT are to insert , delete, store data in RAM.",1.0
-4894,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4895,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,features of ADT are :\n- easy traversal\n- easy deletion \n- easy insertion\n\n,1.0
-4896,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,1.0
-4897,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1. it is a linear data structure\n2. the data is stored in contiguous blocks of memory,1.0
-4898,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,*+ab+cd\n\nab+cd+*,2.5
-4899,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1 helps in storing data\n,1.0
-4900,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4901,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,In interpolation search technique we select an element according to the formula element1 = (left - right)/array[right]-array[left] + right - left\nand then follow the pattern of binary search by checking whether the required element to search is greater than element1 or smaller then we divide the array according\nto the result and repeat the process until we get our required element.,1.0
-4902,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4903,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4904,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4905,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT - is abstract data type. \ndynamic array creation  using pointers \n\n\n\n\n\n\n\n,1.5
-4906,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT stands for Abstract Data Type in which the space is allocated virtually and not in a static way. example: malloc in C and virtual classes in C++.,2.0
-4907,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Abstract data type(ADT), the elements are stored virtually so there is no need for static memory allocation so it takes LESS SPACE for storage. For example, malloc.",2.0
-4908,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"The elements are stored virtually, there is no need for static memory allocation so it takes less space for storage. ",2.0
-4909,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4910,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4911,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4912,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4913,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"abstract data types are made using basic data types such as int ,float ,etc.\ncommon example of abstract data types are struct(structure),class,etc.\nfeatures are :\nin ADT we get some more features like storing different data types in one data type. performing some extra operations as well\n ",2.0
-4914,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4915,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4916,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The main feature of ADT list is that we can specify the domains and operations independently.,2.0
-4917,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,its features are domain and operations are specified seperatly.,1.0
-4918,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4919,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Interpolation search technique is very similar to binary search. In interpolation. we divide the array by using a particular formula till it reaches a point where it can't be divided further and then sort it.\n ,1.0
-4920,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4921,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4922,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4923,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4924,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,abcd is the notation,1.0
-4925,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4926,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Features of ADT:\nADT stands for Abstract data types which means that the construction of that data type is abstracted from the rest of the code.\nThese data types store the address pointer to their successive and previous node, so they improve the retrieval of data from the program. Also, they do not require to store the data together, so they do not require a continuous space in computer memory.",2.0
-4927,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT is abstract data type is data type.\ndefine in some other class and used in some other class.\nused to Override the the working of one function .,2.0
-4928,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT also known as abstract data type is used to store data using different data types.,2.0
-4929,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4930,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The features of ADT are that the data can be divided discretely.,2.0
-4931,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT have the following features:\nThese stores data linearly.\n,2.0
-4932,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it is used to store data linearly,2.0
-4933,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Abstract Data Type (ADT) is a programming concept that allows the definition of a data type, its behavior, and its properties without specifying its implementation details. The features of ADT are:\n\n1. Encapsulation\n2. Abstraction\n3. Modularity\n\n4. Data Independence: ADTs allow the data type to be defined independently of any particular programming language or hardware architecture, making them more flexible and portable.\n\n5. Reusability: ADTs can be reused in different parts of a program or in different programs altogether, which can save time and effort in programming.\n",2.5
-4934,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,they are pre defined data types given in the standard template library of the cpp.,1.0
-4935,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Data is stored in non continuous manner so it can be used to store data which is not yet organized or can be sequentially added in data structures like array,1.0
-4936,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4937,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it is optimized in nature\n,2.0
-4938,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Features - Less time consuming, complete",1.0
-4939,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT has variables and methods(functions) ,2.0
-4940,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4941,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"abstract data types , they are user defined",1.0
-4942,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"It behaviour is designed by set of values\nLike vectors ,list",2.0
-4943,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4944,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,adt means abstract data type. Some of its features are:\n1. encapsulation\n,0.0
-4945,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,IT IS REUSABLE AND ROBUST.\nIT REDUCES CODING EFFORTS\nBASED ON OBJECT ORIENTED PROGRAMMING,0.0
-4946,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4947,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4948,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation," ADT is let us consider in-built data types that are provided to .The features are addition, subtraction, division, multiplication.",2.0
-4949,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4950,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT is used to implement encapsulation in a program.,2.0
-4951,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4952,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4953,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4954,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,the features of ADT are :\nIt exports a type.\nIt exports a set of operations. This set is called interface.\nOperations of the interface are the one and only access mechanism to the type's data structure.,2.0
-4955,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1. It is fast.\n2. it is super easy to implement.,2.0
-4956,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4957,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Some features of ADT are:\n1) It reduces space consumption by the data structure.\n2) It reduces the system time hence making the system faster.\n,2.0
-4958,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4959,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4960,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation, ,0.0
-4961,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,interacive securitiues \naccess control;\nvarious pannel\n,1.0
-4962,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,-,0.0
-4963,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,adjacency matric and list stores the information about the adjacent vertecies such as if it is connected adjacently or not and might even store the weight of the adjacent vertex,1.0
-4964,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,features of ADT(abstract data type) are:\n1. it exports a type.\n2. it exports a set of operations.\n3. Operations of the interface are the one and only access mechanism to the type's data structure.,1.0
-4965,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4966,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4967,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,\n,0.0
-4968,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4969,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it exports a type . it exports a set of opeartions . ,1.0
-4970,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4971,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it is efficient and gives optimal solution.,1.0
-4972,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,It represents different data types through which we can implement the given code.,1.0
-4973,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4974,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"it allows creation  of instrances of data with well defined properties,elements do not need to be unique",1.0
-4975,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4976,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,It is faster\nUses less space,2.0
-4977,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,adt can be an integer type it can be list of integers list of characters and even the list of list,1.0
-4978,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4979,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-4980,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The features of an ADT is that it is an abstract data structure,1.0
-4981,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,public and protected function,2.0
-4982,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"ADT \nabstarct data type\nnon triaval data type\n\nmap,set",2.0
-4983,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-4984,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The features of ADT are  that:\n1-it exports a type\n2-it exports a set of operations. this set is called interference.,1.0
-4985,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-4986,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,It exports a set of operations known as  interface.\nOperations of the interface are the one  mechanism of this  type of data structure.,2.0
-4987,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Abstract data type can change data type that is intended for other purpose. Example in char int, here integer is being treated as character for future references.",1.0
-4988,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-4989,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-4990,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Encapsulation, polymorphism",1.0
-4991,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Abstract data type are the data types we form using class. Or the ones in the STL library. eg: vector, list, queue etc. The operations on these abstract data types are predefined in the STL library.",2.0
-4992,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,encapsulation-binding data with functions\ndata abstraction \npolymorphism-allows multiple information of adts,1.0
-4993,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,These are the prime features of ADT:,1.0
-4994,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-4995,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-4996,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Abstract data types are essential for big scale programming. They package data structures and operations on them, hiding internal details  for eg. an ADT table provides insertion and lookup operations to users while keeping the underlying structure, whether an array, list, or binary tree or other.",1.0
-4997,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Abstract data types encapsulate data and their functions in a single unit,1.0
-4998,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,prefix :  ()()*+ab+cd\n,2.0
-4999,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"ADT or abstract data type can be used to implement data structures of different kind of values stored basically using a template for all the types of structures.\nexample: pair<int, int>, pair<char, char>",2.0
-5000,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5001,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5002,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5003,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5004,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The ADT is a data type which is defined by the user only.\nThe user can initialize the data type in any way it wants.\n,1.0
-5005,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"recursive function is way of calling the same function inside of it, by using which we can create a loop of running a particular process a number of times according to our need.",2.0
-5006,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5007,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,It exports a type.\nIt exports a set of operations. This set is called interface.\nOperations of the interface are the one and only access mechanism to the type's data structure.,2.0
-5008,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5009,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,feature of ADT are that we can create our own datatypes example like struct.,2.0
-5010,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The examples of Abstract Data Type (ADT) are structures and classes.\nIts features are that the programmer can implement his own data types other than the existing/predefined to work more efficiently.,1.0
-5011,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ab+cd+*\n*+ab+cd,1.0
-5012,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5013,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5014,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"public function, proception function  ",2.0
-5015,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5016,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Abstract Data Type short form being ADT is very useful as the data type of a structure is abstract, it can be char, int, long etc. hence making the data structure usable for every type of data, all the operations and functions will work for any system-defined data type",2.0
-5017,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT helps to under stand the program easily,1.0
-5018,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5019,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5020,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it can be used to define a data structure as per our own convenience,2.0
-5021,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,prefix -   ++*abcd\npostfix-  abcd*++,1.0
-5022,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,each set of data is kept independent of other without knowing about the data in other set in adt.,2.0
-5023,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,features the ADT :-\nFind element in faster way,2.0
-5024,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5025,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1.It exports a type.\n2. It exports a set of operations.\n3. Operations of the interface are the one and only access mechanism to the type's data structure.,1.0
-5026,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT stands for Abstract data type . In ADT we store the elements as per their data types.,2.0
-5027,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,abstract data structure defined by the user .,2.0
-5028,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5029,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,
-5030,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5031,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5032,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5033,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5034,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The features of ADT(Abstract data type are)-\nThe node of such data types are defined by using any structure or class and it uses the built in data types to get it's implementation.\nIn a simpler manner we can say that ADT is the implementation of the new data type where the user will not be aware about it's internal implementation.,2.0
-5035,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5036,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5037,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,the main feature of abstract data structure is to extract previous as well as next element in the list. ,1.0
-5038,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5039,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Prefix: *\nPostfix: b,0.0
-5040,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5041,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5042,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Features of abstract data types -  easier insertions and deletions, more storage available for data storing as memory need not be continuous. faster traversals. more ways to store data like trees, hierarchies can be introduced. ",2.0
-5043,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Abstract Data type includes \nabstraction\nencapsulation\nrobust\nbetter conceptualization,2.0
-5044,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"abstract data type are char, int . these data type are in- built data type into compiler for quick and easy use.",2.0
-5045,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Abstraction and encapsulation,2.0
-5046,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"The features of Abstract Data Type  such as int, float, double, long, etc. are considered to be in-built data types and we can perform basic operations with them such as addition, subtraction, division, multiplication, etc. ",2.0
-5047,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5048,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5049,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,The features are:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n,2.0
-5050,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,\npostfix notations:- \,0.0
-5051,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5052,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5053,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT is an act of representing essential features without including the background details.\nfeatures of abstract data types:\n1) it is robust and reusable\n2)It is based on the principle of object oriented programming(OOP)\n3)It can be reused at several places and it reduces the coding efforst\n,2.0
-5054,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5055,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Its reusable, uses object oriented programming.\n",2.0
-5056,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Abstract Data type- ,2.0
-5057,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5058,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Abstract Data type (ADT) is a type (or class) for objects whose behavior is defined by a set of values and a set of operations. The definition of ADT only mentions what operations are to be performed but not how these operations will be implemented. It does not specify how data will be organized in memory and what algorithms will be used for implementing the operations,2.0
-5059,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1. list of integers\n2. list of payrolls\n3. list of prices,2.0
-5060,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT are used to implement private functions.,2.0
-5061,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Its full form is Abstract Data Type. ,2.0
-5062,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it helps in exports various operations and files.,2.0
-5063,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,It exports a type.\nIt exports a set of operations. this set is called interface.,1.0
-5064,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,abstract data type such as array list map queue\nthey are used to store data in different manner\nthey support insertion deletion of elements\nsome are used to tell the frequency of elements such as map\n,2.0
-5065,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5066,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it export a time or it export  a set of operation,2.0
-5067,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"Uniform data structure can be searched through in constant time via interpolation , i.e.  taking mid part  and manipulating it accordingly",2.0
-5068,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1)It exports a type.\n2)It exports a set of operations. This set is called interface.\n3)Operations of the interface are the one and only access mechanism to the type's data structure.,2.0
-5069,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,It is a list type data structure that is used for storing values.\n,2.0
-5070,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5071,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT:\nThese are faster then linear data types.\nThese are  ,0.0
-5072,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5073,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,They can be used to solve bigger problems and reduce the time by not writing the smaller part.,1.0
-5074,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,I am going to assume here that ADT refers to ABSTRACT DATA TYPE.\nThe main feature (or can I say the only feature I KNOW) is encapsulating data and operations into a single page.,1.0
-5075,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5076,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5077,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5078,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5079,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT is useful for storing large data.,2.0
-5080,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5081,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5082,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5083,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,adt stands for abstract data type .,2.5
-5084,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,they can be made according to the requirements of the programmer in order to ease their task.,0.0
-5085,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,It is an auto defining tool.,0.0
-5086,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5087,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT stands for abstract data types . Object whose behaviour is defined by values. ADT's have memory allotted to them by system memory.,2.5
-5088,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Features of ADT are:\ninsertion\ndeletion ,0.0
-5089,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5090,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5091,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5092,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5093,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it is memory effecient and doesn't use consecutive memory blocks. operations also have less time complexity,0.0
-5094,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5095,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5096,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5097,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1. Its node is defined using class.\n,0.0
-5098,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,abstract data type features are :\nused to create virtual class\n,2.0
-5099,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,data structure in data is stored in uncontinous form,0.0
-5100,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5101,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5102,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5103,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Not  known,0.0
-5104,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5105,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5106,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,1) It exports a type\n2) It exports a set of operations,0.0
-5107,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5108,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5109,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"we can perform several functions like insertion , deletion , extract min function etc...",1.0
-5110,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Abstract Data Type : User defined data type \nImplement set of operations defined by the user.,2.5
-5111,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,ADT is user defined in which we use pre existing data structures,0.0
-5112,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,"less time interval, efficient performance",0.0
-5113,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,insertion and deletion are the features of ADT.,0.0
-5114,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,Implementation is hidden\nWe are given some functions which we can invoke on the Abstract Data Structure,0.0
-5115,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5116,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,time interval is low,0.0
-5117,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5118,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,we can move from one location to another in short time of O(1),0.0
-5119,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,it move location in the live time so that's the main feature of  ADT,0.0
-5120,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,,0.0
-5121,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,we move location in content time like o(1)\n,0.0
-5122,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,3,0.0
-5123,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,abstract data type : ex-array\n1)insertion \n2)deletion ,0.0
-5124,Mention the features of ADT.,a. Modularity ([i.] Divide program into small functions [ii.] Easy to debug and maintain [iii.] Easy to modify)  b. Reuse ( [i.] Define some operations only once and reuse them in future) c. Easy to change the implementation,features of adt is:\nit tells the exact pattern from the text with the same text but not the values;,0.0
-5125,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5126,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list ADT is the form to store the elements in the form of a list in a continous manner in the form of a list and in a sorted order,2.5
-5127,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5128,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",A list ADT refers to list abstract data type. it works similar to abstract class but instead of a class it is defined as a linked list,2.5
-5129,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","List ADT is essentially an array with insertion, deletion and updation methods defined. In a normal array there is no provision to increase the size of array but in ADT list we can do that.",2.5
-5130,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is used to store data in abstract form ,1.0
-5131,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","a list is an abstract data type that describes a linear collection of data items in some order , in that each element occupies a specific location in it.",2.5
-5132,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",It makes the list dynamically.,1.0
-5133,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5134,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5135,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5136,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is similar to array ADT but the only difference is that list attach the nodes to the next node and created the list dynamically .,2.5
-5137,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is a list which perform above operation.,2.5
-5138,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5139,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","Array Data Structure , ADT is a linear data structure that covers  a space of n bits where n is the number of elements stored in it.",1.5
-5140,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Linked List is a collection of linearly connected node where each node stores the address of the next node.,1.5
-5141,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",A linked List implemented using array is called a List ADT,1.5
-5142,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",It's one of the main feature is that it stores the data in contiguous blocks of memory.,1.5
-5143,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list is data structure which is used as array to store elements effciently.,1.5
-5144,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5145,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5146,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Abstract data type is a mathematicaL MODEL. abstract data type  is defined by its behaviour from the point of view of the user,1.5
-5147,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",ADT is Abstract Data Type with list data structure to perform normal basic operations on a particular program,1.5
-5148,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5149,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","ADT - is abstract data type \nprobably a double pointers \nor dynamic array creation \nmalloc() , calloc() in c and new and delete in cpp",1.5
-5150,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Linked List is an example of ADT. In liked list each element has the address of the next element and so on. So the list is not allocated statically and we need to traverse each element from the start or end to find an element.,1.5
-5151,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Linked list is a form of ADT because the elements are not static and all of them have a pointer which points to the next element. ,1.5
-5152,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","Abstract Data Type ( ADT) is a form of linked list as elements are stored virtually, using pointers which point to the desired element. No static memory allocation is required.",1.5
-5153,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5154,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5155,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5156,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5157,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5158,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5159,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5160,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",ADT list stands for Abstract Data Type List.,1.0
-5161,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is Abstract Datatype list.,1.0
-5162,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Its Abstracted Dynamic List.,1.0
-5163,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Prefix-*+ab+cd\nPostfix-ab+*cd+,0.0
-5164,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5165,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5166,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List adt is formed by linearing connecting linkedlist nodes.,1.0
-5167,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5168,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5169,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5170,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is a type of data structure similar to the list we use in python where a key has a corresponding value to it.,1.5
-5171,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5172,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is defined as the list of different abstract data types used to store the data.,1.5
-5173,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5174,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",ADT list are a type of data structures in which data is stored in the non continuous order.,1.5
-5175,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is used to store data one after the other just like array but with in a pair. Example: Dictionary in python stores data in key value pair.,1.5
-5176,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",It stores data same as array but in a pair.,1.5
-5177,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","List ADT (Abstract Data Type) is a collection of data elements that are arranged in a sequential order, and each element can be accessed by its position or index within the list. The List ADT supports operations such as inserting, deleting, and accessing elements in the list. Lists can be implemented using arrays or linked lists, and they can be used to represent various types of data, such as strings, integers, or complex objects. ",1.5
-5178,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",The list adt is where we can store multiple heterogenous sequence of elements in a array like form,1.5
-5179,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Linear data structure in which the data is stored in non continuous manner.,1.5
-5180,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5181,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5182,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT helps you to store data in a list which uses linked list and hashing.,1.5
-5183,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","List ADT would have insert, delete, update, display functions",1.5
-5184,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5185,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",they are user defined list data structures like linked lists,1.5
-5186,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list is an abstract data type in which data is stored in ordered way so that we can do efficient retreival,1.5
-5187,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5188,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5189,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","IT  IS UESD TO DESCRIBES A LINEAR COLLECTION OF DATA IN SOME ORDER , SUCH THAT EACH ELEMENT HAS A SPECIFIC POSITION.",0.0
-5190,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5191,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5192,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element"," ADT is let us consider different in-built data types that are provided to us. Data types such as int, float, double, long, etc. are considered to be in-built data types and we can perform basic operations with them such as addition, subtraction, division, multiplication. ",1.5
-5193,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5194,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5195,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5196,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",linear data structure in which the data is stored in a non continuous manner,1.5
-5197,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5198,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","list ADT is defined as List defines the member functions that any list implementation inheriting from it must support, along with their parameters and return types.",1.5
-5199,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5200,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5201,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is a method of storing and compressing list to avoid overuse of system space by the list.,1.5
-5202,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5203,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5204,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element", ,0.0
-5205,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",IN ADT in this the properties are assign to each element ,1.5
-5206,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",-,0.0
-5207,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",adjacency list is an array whose each index is connected to a linked list which stores the information about the adjacent vertecies,2.0
-5208,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5209,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5210,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5211,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",adt stands for abstract data type that stores collection of data linearly.,1.5
-5212,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5213,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5214,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5215,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","in ADT, ",1.0
-5216,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","ADT stands for Abstract Data Types. It is a list of different data types like - long, int, float, double, char, bool",2.0
-5217,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5218,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","Absract data type, it allows creation  of instrances of data with well defined properties,",1.0
-5219,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5220,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5221,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list adt refers to list of any integer type where the properties are independently implemented,2.0
-5222,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5223,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5224,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","A list is a data structure in which may be having multiple or a large number of nodes and the nodes may itself be a list or a linked list, or a simple element such that the value at the node points to the unique element.",2.5
-5225,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","data types such as stack, queues and linked list",2.0
-5226,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","map<int,int>mp;",1.0
-5227,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5228,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is the linear collection of data items in some porder in that each element occupies a specific positions in the list.,2.0
-5229,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5230,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","A list is an abstract data type that has a linear collection of data items in some order, in this each element occupies a specific position in the list. The order could be alphabetic or numeric or it could be the order in which we have added the list.",2.0
-5231,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List Abstract Data Type,1.0
-5232,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5233,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5234,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List adt or list abstract data type is the one with collection of data in orderly fashion where each data is positioned within the list,2.0
-5235,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List is like an array. the data is stored in contiguous memory locations and retrieved in that manner too. ,2.0
-5236,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list abstract data structure that represents the ordered collection of elements where each element is identified by its position or index.,1.0
-5237,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",ADT ,1.0
-5238,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5239,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",reperesnts elements in the form of list of elements. ,2.0
-5240,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","In ADT ,each list element must have some data type. In the simple list implementations, all elements of the list are usually assumed to have the same data type, although there is no conceptual objection to lists whose elements have differing data types if the application requires it. The operations defined as part of the list in ADT.",2.0
-5241,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is a dynamic array which means that it's size need not be pre defined and data entry could go being done,2.0
-5242,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","Abstract data types  are mainly used for large-scale programming. They package data structures and operations on them, hiding internal details.  ADT table provides insertion and lookup operations to users while keeping the underlying structure, whether an array, list, or binary tree.",2.0
-5243,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5244,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element"," A linear relationship means that, except for the first one, each element on the list has a unique successor. Also lists have a property intuitively called size, which is simply the number of elements on the list. Know that every list has a size.",2.0
-5245,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5246,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5247,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5248,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is a user defined lit which is solely initialized by the user itself. ,1.0
-5249,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",median search is also know as interpolation search technique,1.0
-5250,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5251,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","A list is an abstract data type that describes a linear collection of data items in some order, in that each element occupies a specific position.",1.0
-5252,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5253,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","list adt contain data variable, pointer to next node .",1.0
-5254,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Basic List ADT contains a node and a pointer to its next node. The last node's next pointer points to NULL.,1.0
-5255,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5256,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5257,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",using list in place of nodes which helps in connecting several nodes of the ADT among eachother whcih make sit more efficient andmore useful via better traversal.,2.0
-5258,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","link list, queue, stack, data structer in c",2.0
-5259,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",a datatype whose properties are specified independent of any particular implementation,1.0
-5260,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List is a linear data structure in which there are multiple nodes which have an element in them and a next pointer which points to the next element in the list after them. There is one head in list before which there is no element and then there is one tail pointer after which there is no element.,2.0
-5261,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5262,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5263,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5264,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list adt stands for list abstract data type,2.0
-5265,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5266,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list adt is basically a chained linked list.,2.0
-5267,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5268,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5269,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","List ADT is an abstract data type that describes a linear collection of data items in some order, in that each element occupies a specific position in the list.",1.0
-5270,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",In list ADT we store the abstract data type in a list of elements. ,1.0
-5271,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",linked list in which user can can any type of input.,2.0
-5272,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","ADT refers to abstract data type, Class is  one of classic example of abstract data type features of it are we can make its obj and make inbuilt functions.",1.0
-5273,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,
-5274,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5275,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5276,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5277,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5278,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is a data type which store the elements on different location and stores a pointer to correct the location.l,1.5
-5279,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5280,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5281,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","it is a type of abstract data structure which is in the form of list , like linked list.",1.0
-5282,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5283,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5284,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5285,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","data is stored in a list which has head structure consisting of a count,pointer and address",1.0
-5286,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is linked list. Linked list is a linear data structure in which data is stored in fragments in the memory but can be accessed as they are linked using  a pointer. this is more realistic as a continuous block of memory for storage may not be available. Especially if size needed is large. \n,2.0
-5287,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","The data is stored in key sequence in a list which has a head structure consisting of count, pointers and address of compare function needed to compare the data in the list is called List ADT.",2.0
-5288,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5289,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",It stands for Abstract Data Type.\n,1.0
-5290,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5291,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5292,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5293,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5294,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5295,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5296,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5297,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5298,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5299,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5300,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5301,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5302,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5303,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list adt is a abstract data type where the format of data items follow linear fashion,1.0
-5304,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",ADT include static int.,1.0
-5305,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Its full form is Abstract Data Type. ,1.0
-5306,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",it is abstract data structure in which data is not stored in continuous way.,1.0
-5307,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","ADT stands for abstract data type. The data is generally stored in key sequence in a list which has a head structure consisting of count, pointers and address of compare function needed to compare the data in the list.\nThe data node contains the pointer to a data structure and a self-referential pointer which points to the next node in the list.\n",2.0
-5308,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list is an dynamic array whose size varies according to the number of times user enter data in it\nwe dont need to define its size at the time of its creation,2.0
-5309,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5310,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",a abstract data type whose properties (domains and operational )are specified independently of any particular implementation.\nlist ADT  are linear structure on which data is stored in a non continous fasion,2.0
-5311,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",*+ab+cd and ab+cd+*,0.0
-5312,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element"," A list is an abstract data type that describes a linear collection of data items in some order, in that each element occupies a specific position .Here the data is stored in a non - continuous fashion.\n",2.0
-5313,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5314,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5315,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","Abstract data types are the data types which are abstracted for primitive data type\nfor eg: trees , graph etc.",2.0
-5316,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5317,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",These are derived from primitive data types.\neg: graph,1.0
-5318,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","Using the same assumption of ADT = ABSTRACT DATA TYPE...\nUsing that analogy or conclusion, we can say that: \nAn abstract data type is defined by its behavior from the point of view of a user, of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations.",1.0
-5319,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5320,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",obtained from primitive data type,0.0
-5321,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",ab+0cd+0*      - postfix\n*0+ab()           - prefix,0.0
-5322,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5323,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is a data structure used to store data in list form,1.0
-5324,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5325,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",a list is a data structure in which one element points towards the next element,1.0
-5326,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5327,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list of adt is,0.0
-5328,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","it is a user defined data structure, made using already existing data structure.",
-5329,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element","ADT- Stands for Auto Defining Tool, it defines the further list on its own.",0.0
-5330,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5331,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT's have data stored in non-continious form . These contain nodes and nodes are connected to each other and nodes contain the address of other node.,2.5
-5332,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5333,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",data stored in list ,1.0
-5334,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5335,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5336,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5337,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",it is an abstract data structure which is a user defined data structure made using existing data structure  to fit the requirements of the user.,1.0
-5338,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5339,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5340,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5341,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",List ADT is an ADT in which all elements have pointer to first element.,0.0
-5342,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5343,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",linear data structure in which data is stored in uncontinous fashion,1.0
-5344,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5345,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5346,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5347,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",Not known,0.0
-5348,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5349,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5350,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",An abstract data type which is defined by its behavior from the point of view of a user of the data,1.0
-5351,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5352,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5353,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list in adt is a queue in which all element s are stored,0.0
-5354,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",An ADT List can be defined by an user by giving set of operations can be dynamic.,0.0
-5355,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",user defined data structure in which we use lists,0.0
-5356,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5357,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5358,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",A List ADT or List Abstract Data Structure is an implementation of the List data structure which only provides the developer with the necessary functions and attributes to use the list but the developer is unaware about how the list is implemented internally,2.5
-5359,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5360,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5361,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5362,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",lists which have values and names\neg. classes,0.0
-5363,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",the list of  adt is  like contain the roll no name etc,0.0
-5364,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5365,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list is having name and value\nlike \,0.0
-5366,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",,0.0
-5367,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",list is data structure in which memory is stored in a node and connected from another node .and their is starting node and an ending node ,1.0
-5368,Define List ADT,"A list is a sequence of zero or more elements of a giventype. The list is represented assequence of elements separated by comma. A1, A2, A3…..AN Where N>0 and A is of type element",automata daste theorem,0.0
-5369, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,The type of linked list in which the tail of the last node is linked to the first node(head) is referred to as a circular lionked list.,2.5
-5370, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,when the head of a linked list is connected with the tail of it it is called as circular linked list,2.5
-5371, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in circular linked list the head of the first node is conencted to the tail of the last node ,2.5
-5372, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is like a normal linked list but the the tail of the last element is connected with the head of the first element,2.5
-5373, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked List is a LL in which the last node of the list instead of pointing to null points to the head of the LL. ,2.5
-5374, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked List is a linked list data structure where its head is connected to its tail part.,2.5
-5375, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is the  variation of linked list in which first element points to the last element and the last element points to the right element. Both singly linked list and doubly linked list can be made into a circular linked list.,2.5
-5376, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,In circular linked list the right of the last element of the linked list is the first element of the linked list,2.5
-5377, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular link is a link list whose end node is linked with the starting node means there is no start or end of the circular link list.,2.5
-5378, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"the circular linked list is a linked list where all nodes are connected to form a circle. In a circular linked list, the first node and the last node are connected to each other which forms a circle.",2.5
-5379, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A list in which the first and last node are also connected with each other is called the circular linked list. ,2.5
-5380, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular list is the list that is connected in a way that the last node address is stored in the first node.,2.5
-5381, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is the data structure which stores the information in nodes form which are connected to each other and in this the last node is connected to its parent node forming a CIRCLE.,2.5
-5382, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"A circular linked list is the one in with the initial node (head) carries the details of both previous and next node...the last and first node are also connected , as in we can traverse this format of linked list from both the sides which is different from a doubly or singly linked list where final and initial point are not connected and only way to reach initial node from final  node is to traverse the entire length again.",2.5
-5383, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list whose head and tail are interconnected to each other is called a circular linked list.,2.5
-5384, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Special type of linked list where last/tail node stores the address of the head node,2.5
-5385, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list which has the last node of the linked  list connected to the first node of the linked list...this way forming a circular loop and hence called circular data structure,2.5
-5386, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5387, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is link list which  connects last element to the first element .here the element can be inserted in between or back of any element.\n,2.5
-5388, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list which connects that node of the last element to the head of the first element.,2.5
-5389, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5390, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linear data structure whose first and last node is the same. In other words in a circular linked list the tail element of the list is connected to the head element forming a circle.,2.5
-5391, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list in which the node of last element is connected to the node of  first element. that is the last element node is not null and after traversing through the last element it will again start pointing towards the first element. it forms like the close chain.,2.5
-5392, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5393, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list tail pointer and it does not have a head pointer or consider it does not have any end as its end index have address of first node in linked list .\n,2.5
-5394, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is when the last element of the list is connected to the first element of the list so we keep traversing in a circle.,2.5
-5395, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"When the leaf node of a linked list points to the root node of the linked list, it is known as a circular linked list.",2.5
-5396, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"When the lead node is of a linked list points to the root node, this is known as a circular linked list.",2.5
-5397, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a type of linked list whose tail always point to the head of it which is the reason for creation of circular loop.,2.5
-5398, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"In a circular linked list, the last node does not have a null next pointer. It points to the head of the linked list.",2.5
-5399, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is a special type of linked list in which the last element is connected to the first element forming a loop that is why it is known as circular linked list,2.5
-5400, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is where the last node store the address of first node. \nexample:\n1-2-3-5-1,2.5
-5401, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a linear data structure which is same as linked list the only difference is that the tail of linked list connected to the head of linked list which results in circular linked list . ,2.5
-5402, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,The circular linked list is a special case of linked list in which last node of the list is connected to the first node again forms a complete loop and from last node we go back to 1st node is called circular linked list.,2.5
-5403, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked  is type of data structure in which last element points to the first element . \nit uses pointer approach . ,2.5
-5404, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"A circular linked list is a modified single linked list in which the last node points to the first node. So, if we traverse a circular linked list then the next element to the last node will be the first element and the linked list will continue again.",2.5
-5405, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is the one in which starting and last element of linked list are linked to each other.,2.5
-5406, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A  circular Linked list is a kind of linked list in which the head and the tail of the linked linked are connected .,2.5
-5407, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5408, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"A circular linked list is a linked list in which the next pointer points to the first element or the head node, instead of end.",2.5
-5409, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"A linked list, in which the last element has a pointer which points towards the first element making the complete linked list as a circle is known as circular linked list.",2.5
-5410, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Linked list is the kind of data structure where each node has a pointer pointing to its next node or previous node along with its own data. Those linked list where the end node is connected with the root or starting node of to form a complete cycle is called a circular linked list.  ,2.5
-5411, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"circular linked list is kind of linked list in which first and last value of the array are same, in other words head and tails of the linked list array are same then the given linked list is called as circular linked list.",2.5
-5412, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5413, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A connected array where each of the successive array nodes are connected to the next array pointer .This is known as circular linked list.,2.5
-5414, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"A circular linked list is a linked list having a last node whose tail points towards the head of the first node, so as to obtain a linked list which is has its last node linked to the first node.",2.5
-5415, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A Circular Linked List is a type of Linked list where the last node or the tail node is connected to first node or head node.,2.5
-5416, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A linked list in which the tail pointer points the head pointer of the linked list is called a circular linked list.,2.5
-5417, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list in which the first and last nodes are connected.,2.5
-5418, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a type of linked list in which first and last node of the linked list are connected to each other.,2.5
-5419, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Linked List is a data structure used to store information and is connected using next or tail pointers. Circular Linked List is a type of linked list that is connected like linked list using tail or next pointer but that last node of circular linked list stores the pointer to the first or head node. In this way it forms a loop which is circular. Circular linked list can be single or doubly in terms of connections.,2.5
-5420, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a type of linked list in which last node of linked list stores the address of the first node of linked list.,2.5
-5421, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"A circular linked list is a type of linked list data structure where the last node points back to the first node, forming a circular loop. This means that the elements of the list are connected in a circular manner rather than in a linear manner, allowing for more efficient traversal and manipulation of the list. Additionally, circular linked lists can be used to implement circular buffers and queues.",2.5
-5422, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circularly linked list is the one where the last element of the linked list instead of being connected to none is connected to the first element of the linked list hence forming a circle.,2.5
-5423, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is a linked list in which the last node points to the first node . It is a data structure that can help access the first element after the end of list,2.5
-5424, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is one with the last node points back to the first node resulting in a loop.,2.5
-5425, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is a kind of doubly linked list where the head and tail of the lined list is connected and forms a loop like structure.,2.5
-5426, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"Circular linked list is a linked list whose tail is connected to the head. \nAfter traversing the whole linked list, when we get to the tail and then traverse tail->next, we'll get head. ",2.5
-5427, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a linked list in which the last node points to the first node,2.5
-5428, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is the lined list in which first node is connected with the last node.,2.5
-5429, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,the last node is connected to first node,2.5
-5430, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is a linked list in which the terminating node is connected to the begin node too also the neighbors are connected to each other creating it circular linked list,2.5
-5431, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list which end and start are connected with each other is called linked list,2.5
-5432, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is a type of linked list where the last node points to the head of the linked list.,2.5
-5433, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"In a circular linked list each data element points towards both its left and right element , even the leftmost points towards the rightmost as its left present element, and the rightmost points towards the leftmost as its right present element.",2.5
-5434, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list whose first element is linked back to the last,2.5
-5435, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a type of data structure where the head node of a linked list is connected to the tail of another linked list.,2.5
-5436, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,In the circular linked list the first element of the array is connected to the next element also wth the last element of the array .Hence we can travel last node to the first node .And we can assume the circular linked list is like a circle . ,2.5
-5437, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is linked list which is circular in nature in which the first and the last element are connected ,2.5
-5438, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"A circular linked list is a type of linked list where every node in the list can access its previous and next node. Also, the last node in the list points to the first node in the list, hence forming a complete chain, or circle. ",2.5
-5439, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a type of linked list in which the last element of the list is linked to the first element of the list. There is no end to this list.,2.5
-5440, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list in which the first and last element are also linked.\n,2.5
-5441, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a type of linked list in which the tail of the linked list points to the head of the node.,2.5
-5442, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is a list in which the tail of the last element of the list bpoints towards the head of the first element of the list,2.5
-5443, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Linked list where its last element is connected to the first element is called circular linked list.,2.5
-5444, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,In a circular linked list the last element i.e tail/end points to the first element of the list i.e head/start.,2.5
-5445, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list with each of its pointers pointing towards its next node the end node points back to the start node in a circular fashion.,2.5
-5446, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is a list in which the last node of the list in connected to first node of the list to form a circle\nthis is done by pointing the next of the last node to the first node in the list,2.5
-5447, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"The circular linked list is a linked list where all nodes are connected to form a circle. In a circular linked list, the first node and the last node are connected to each other which forms a circle. There is no NULL at the end. ",2.5
-5448, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"a circular linked list is where the head of the list and the tail(last node) are connected with each other, forming a circle. \nit can be of 2 types: \n- singly circular LL (tail points to head)\n- doubly circular LL (tail points to head and head points to tail)",2.5
-5449, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in the circular linklist ther is head and tail the staring node iscall head and the end node id call tail if they both are connected using the linklist then this is called circular linklist \n,2.5
-5450, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"In a circular linked list, the first node contains the memory of the last node and the last node contains the memory of the first node, so it forms a cycle/loop",2.5
-5451, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in a circular linked list the last node points to the head node creating a circular list.,2.5
-5452, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"Circular Linked list is a type of linked list in which the last element of the list points back to the first element and the first element points to the last element as well, forming a cycle.",2.5
-5453, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is when tail of the list is connected to the head of the list,2.5
-5454, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is a linked list where the last element is connected to the first element thus last element does not point to NULL.,2.5
-5455, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list has it's end node connected to it's start node providing option for the whole list to be traversed starting from any node. ,2.5
-5456, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked List is the data structure that has ending node connected to the head node of the linked list.,2.5
-5457, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is the linked list where last  root node points to the data first node. ,2.5
-5458, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is a linked list where first element points to the last element and last element points to the first.,2.5
-5459, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is the doubly linked list in which last and first element are connected.,2.5
-5460, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked List is a link list in which the last node of the link list points to the first element of the link list,2.5
-5461, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"Circular linked list are the linked list in which the next node of last node of our list is the first node our linked list , by which they make a circular linked list. ",2.5
-5462, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is a type of double link list where tail's next is head and head's previous is tails.,2.5
-5463, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a list that circularly moves and that do not have an end point its last node is connect to the first node of the linked list\nit uses in music players,2.5
-5464, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is the one whose last element points to the first element.,2.5
-5465, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circularly linked list is that list where all the list are attached to one another and even the last list is connected to the first list\nend list node is connected to the first node,2.5
-5466, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5467, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5468, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list whose last element points to the start element its next element.,2.5
-5469, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,where end of the linked list is connected to the head of linked list.\nin this way while reaching an end of the linked list we move to the first element again as it connected.,1.0
-5470, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked List is a linked list in which last and start node is connected ,1.0
-5471, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,when last index of linked list contains the address of the first index then the last value and first value is connected and hence a circular linked list .,1.0
-5472, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a linked list where all nodes are connected to form a circle.  In circular linked list first node and last node are same.,2.0
-5473, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list in which last node has the address of the first node or root node.,1.0
-5474, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is type of link list in which end node next represent first node(end->next=head-start) it form in circular \nend node point toward first node,2.0
-5475, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,The tail of the previous element in double linked list is connected to head of the next element.,2.0
-5476, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"Circular linked list is a type of linked list which does not have any head or tail, we can start from any position and traverse in the whole linked list. Every node is connected with its next and previous node.",1.0
-5477, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,the linked list in which the tail of last node of the list is connected to head of some other node.,2.0
-5478, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is just a singly/doubly linked list with its head and tail being connected with each other.,2.0
-5479, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is the list in which the last element stores the location of the first element. Basically there is no start or end point in a circular linked list.,2.0
-5480, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is a singly  or doubly linked list in which head  and tail are joined with each other.,1.0
-5481, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"Circular linked list have two pointers. i.e. Head and Tail pointer. And the tail is directly connected to the head, creating a circular manner of flow.",2.0
-5482, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,
-5483, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,last element's next element is the first element.,1.0
-5484, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is a type of linked list where the end of the list is connected to the start of the linked list.,1.0
-5485, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a non linear data structure having its frond and rear node connected,1.0
-5486, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,
-5487, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a type of linked list where the next of the terminal node is connected to root node.,1.0
-5488, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,when the ending node of a linked list points to the first node of the same linked list that is called a circular linked list,1.0
-5489, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a type of list in which the last node (tail) is linked to the first node (head) and it is in a form of circle (when we visualize it). ,1.0
-5490, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is a linked list whose last node's next pointer points to the head of the list.,2.0
-5491, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a type of linked list in which we have 2 pointers connected to each i.e one head and one tail ,2.0
-5492, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A linked list in which starting node and ending node are connected thus giving it a circular behaviour.,1.0
-5493, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,
-5494, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a type of linked list in which the first and the last nodes are also connected to each other to form a circle.,1.0
-5495, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list whose front node and last node is also linked by a pointer like all other interconnected nodes and when you reach the end node and try to access the next element you will reach at first node. So it can be a representation of a circle since first and last end is connected or tied together and there is a loop.\n,2.0
-5496, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in circular linked list the next pointer of the last node is connected to the starting node so that we can avoid out of bound condition while traversing.,2.0
-5497, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,we the last element's next node contain the pointer value of first node in a linked list is called circular linked list,2.0
-5498, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,In circular linked list the last node's next pointer points to the head node making the traversal seem like a circle.,2.0
-5499, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,
-5500, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,
-5501, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list where the first element has the address of the last element in it's PREVious POINTER and the last element of the list has the address of the first element in it's\nNEXT pointer.  It has no ending and is circular in nature.,1.0
-5502, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"travelrseler, insertion,  introduction",1.0
-5503, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,the linked list in which the elements are arranged in a circular way that are called circular linked list,2.0
-5504, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a list which has the tail of the list connected to the head of the list that is the next of the tail is the head of the list.,1.0
-5505, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a type of linked list in which the first element of the list is connected to the last element of the same list.,1.0
-5506, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a list in which the last node of the list has the address of the  starting node,1.0
-5507, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A linked list in which the last element's next pointer points to first element of the list.,1.0
-5508, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is a linked list which is circular in nature as 1 2 3 4 5 1 2 3 4 5(example),1.0
-5509, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,
-5510, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in circular linked list the pointer of last node points again back to the first node forming loop.,2.0
-5511, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is special case of linked list in which the last node points to the starting node.,1.0
-5512, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is the one in which the last node is connected to the first node thus making it a circle. ,2.0
-5513, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a singly/doubly linked list where the next node of the tail node is pointing towards the head node of the Linked List.,2.0
-5514, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,The last node  point to the first node and thus makes the linked list circular.,1.0
-5515, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list in which its tail and head are same or the tail is pointing to the head.,1.0
-5516, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Abstract data type LIst is used to store data in form a list.,2.0
-5517, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is the data structure wherein the last node holds the reference of the first node.,2.0
-5518, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a type of data structure where the first node is also connected to the last node. That is all the elements are connected in a circular manner. ,1.0
-5519, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"In circular linked list, the  last element points again to the first element instead of pointing to NULL.",1.0
-5520, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a linked list where the last node of the list has the address of the first node of the list.,1.0
-5521, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is a list where the last element is connected to the first element.,1.0
-5522, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,The circular linked list is the link list whose end node is corrected with its first node and hence we can traverse the entire link list by starting from any node.,1.0
-5523, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in a circular linked the last element(tail) of the linked point to first element(head) of the linked list.,1.0
-5524, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a linked list where the last element of list points to first element as next making a circle like connection.\n,1.0
-5525, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"it is a type of linked list which has no end ,its last node points to the head of the linked list hence its circular in nature.",1.0
-5526, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular flow is created as the tail pointer points to the address of the head. ,1.0
-5527, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5528, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a linear data structure in which the tail pointer points to the address of the head. hence creating a circular loop.,1.0
-5529, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked listis a linked list in which when traverse through linked last and when it ends it starts with the same beggining again,1.0
-5530, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is in which the last node points to the first. In linked list there are two pointers. One pointing to the address of the data and the other to the address in the memory where the next element is stored. So the Tail pointer will point to the head node. ,2.0
-5531, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A linked list in which the rear of the last node points to the front of the first node is called circular linked list.,1.0
-5532, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in circular linked list the end element of the list is connected to the first element of the list.,1.0
-5533, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is the type of linked list in which the data and address of the last inserted element is connected to the head of first element.,1.0
-5534, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list are the linked list whose head element is connected with the tail element of the data,1.0
-5535, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,In circular linked list the first node and the last node are connected to each other forming a circle where from last node ponter ponts to the first node of the linked list. ,1.0
-5536, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list whose start and end nodes are connected is called a circular linked list,1.0
-5537, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a linked list which is also aa doubly linked list the. In doubly linked list the tail of the previous box is connected to the the head of the next box of we can say that tail of previous box is equal to head of the next box. But in circular linked list on thing is extra that makes it more special that is the tail of the last box of the linked list is connected to the head of the first box of the linked list,2.0
-5538, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5539, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a list where the end of the list is connected to the start of the linked list.\n,1.0
-5540, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,The circular linked list is a type of data structure in which the tail node and head node of first and last linklist are connected to each other,1.0
-5541, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is the linked list which has no specific starting or end nodes and here the last node is connected to the first node.,1.0
-5542, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list in which the tail node is connected to the head node in a circular fashion it is called a circular linked list,1.0
-5543, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is the one in which the next of the end node is connected to the first node.,1.0
-5544, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A linked list which connects last element with the first element is called circular linked list,1.0
-5545, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list a doubly linked list in which the a cycle is formed between the nodes . That is the next pointer of the last node is linked to the 1 st node and the previous pointer of the 1 st node is linked to the last.,2.0
-5546, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list in which  nodes are connected in a circular manner. in which starting node and final node are connected.,1.0
-5547, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list where the last node  is connected to the head or the starting node forming a circle is called as a circular linked list. it can be single as well  as double,1.0
-5548, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a kind of list in which the last element contains the pointer to its head element.,1.0
-5549, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list have no head nor tail. It is visits each node in the circular manner and it never ends.,1.0
-5550, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linkedlist a linkedlist in which every node is connected to its cosecutive one and also last node is connect to its first node.,1.0
-5551, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a regular linked list except that the last element is also connected to the first element.,1.0
-5552, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list in which the pointer of tail node points to the head node\nno node in circular linkedlist points to null,1.0
-5553, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked list is the Linked list  in which last node is connected to root node of linked list  ,1.0
-5554, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is based on the concept of linked list only and main difference is that in this last node is connected to the first node and the whole concept of linked list is same that every node is connected to other node,2.0
-5555, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is a type of data structure that is used to store values in a list based form which was developed very early on Ada lovelace,1.0
-5556, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked List is a type of Linked list in which the first element points to the last element and the last element points to the first element. Both singly Linked List and doubly Linked List can be made into a circular linked list.,2.0
-5557, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"The Linked List data structure in which the ending point has the pointer address to the starting point. So when we try to traverse, it develops a circular pattern.",1.0
-5558, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a data structure similar as linked list in which the next pointer of the last node pointing to the head of the linked list.,1.0
-5559, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is a modification of linked list in which last element is connected to starting node and each node have two pointers which points previous and next element of linked list.,1.0
-5560, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,The linked list wherein the first and the last elements are connected is called a circular linked list.,1.0
-5561, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is a type of linked list in which the last node is connected to the starting node forming a circle.,1.0
-5562, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular Linked-list can be defined in two ways:\nA singly linked linked-list whose \,1.0
-5563, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a linked list in which the last node is having the address of the first node instead of null in its next pointer.,1.0
-5564, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,when the end node is linked to the first node forming a circle it is known as circular linked list,1.0
-5565, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5566, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in circular linked list the last node is connected to the starting or first node.,1.0
-5567, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a type of linked list in which the end node is connected to the first node i.e. last node stores the address of the first node.,1.0
-5568, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"it a type of data structure ,modified linked list which stores the address of both the previous element as well as the next element.What makes it circular is the fact that the last element/node /link stores the address of the first element in next element's address and the first element/node /link stores the address of the last element in the previous element's address.",2.5
-5569, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"In circular linked list, the last node of a linked list points towards the first element of that linked list.  ",2.5
-5570, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,In circular linked list the last element of linear linked list is connected to the head of the linear linked list.,2.5
-5571, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,In circular linked list the last node of the linked list is connect with the head of the linked list that means no permanent head for the circular linked list.,2.5
-5572, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list where the last node's next points to the head node's previous pointer.,2.5
-5573, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A linked list where last node points towards the first node.,2.5
-5574, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"circular linked list is the linked list in which the element after the last element is the first element, like a loop",2.5
-5575, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked list has its last pointer pointing the to the head of the linked list . Due to which on iteration of linked list it forms loop.,2.5
-5576, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,there are 3 types of linked list circular linked list is one of them in which we can traverse through nodes in a circular path ,2.0
-5577, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5578, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Linked list whose head points to the tail node instead of NULL.,1.0
-5579, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is one of a type of linked lists whose next pointer of the last node is connected to the head pointer. It is a totally connected linked list in which all the nodes are mutually connected to each other.,2.5
-5580, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a circular linked list is a linked list in which the last node has the next index of first node,2.0
-5581, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is a linked list in which tail nodes next is head node,2.5
-5582, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list a type of linked list data structure in which a cycle gets formed due to the connection between the pointers of first and the last node of a linear linked list.,2.5
-5583, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked List is one of the type of Linked List where the tail-node next pointer is linked with the head of the linked list. It can also be of two types Singly-Linked List or Doubly Linked list.,2.0
-5584, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list in which head pointer to null pointer null is the head pointer of the other ,0.0
-5585, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,It is a linked list in which the address pointer of the last node stores the address of first node.,2.5
-5586, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,in a circular linked list the node of the last is pointing  to the  data of the first,2.5
-5587, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is collection of nodes in circular manner ,0.0
-5588, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list in which last node is linked to the first node of the list ,2.0
-5589, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is such a linked list in which the tail of the last node points to head of the first node and it moves circularly.,2.5
-5590, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular Linked List is a type of Linear Data Structure in which pointer from the leaf node is connected to the root node and vice versa.,2.5
-5591, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list is defined as the linked list where our linked list moved in circular ring ,2.0
-5592, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list a normal linked list but starting node can be accessed from the last node and vice versa,2.5
-5593, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Linked list where all the nodes are connected to left as well as their right counterparts and the first node's left node would be the last node and the last node's right node would be the left node is called circular linked list.,1.0
-5594, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is list in which the last pointer of linked list is pointing the first element of linked list,2.5
-5595, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,,0.0
-5596, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list., A circular linked list is linked in which the ending point(null node is co0nnected to the starting point(head node),2.0
-5597, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,linked list in which all child nodes are connected ,0.0
-5598, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"In a circular linked list, its last pointer will point towards its first node.",2.5
-5599, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A linked list in which there is no end pointer and every pointer is connected to a list,0.0
-5600, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,first pointer of element connected to last pointer of element in list is called circular linked list,2.0
-5601, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is a type of list which have the same starting and ending point.,2.0
-5602, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,A circular linked list is a set of nodes pointing to each other and the last node points to the first node .,2.5
-5603, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,Circular linked list are connected linked list from head and body by other linked list,0.0
-5604, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,it is the one in which address of  last node of single linked list is again connected to the previous address of first node and a ;loop is made .,2.5
-5605, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"the linked list in (which it has 2 nodes first to store data of elements and second is to store address the next elements ) in a circular way , in which last elemnt can be first or first can be last",2.0
-5606, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,"In a circular linked list, the pointer of the last node points at the address of the first node.",2.5
-5607, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular liked list is the method of interesting a data in the form of LinkedList into a database ,0.0
-5608, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,a linked list which is connected end to end toi its start and end points,0.0
-5609, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list., =122;,0.0
-5610, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is the implementation of data.,0.0
-5611, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,the linked list in which ending node is connected to starting node and which creates a loop ,2.0
-5612, What is a circular linked list?,A circular linked list is a special type of linked list thatsupports traversing from the endof the list to the beginning by making the last node point back tothe head of the list.,circular linked list is the linked list in which the last node is again connected to the first node and it make a loop eventualy.,2.5
-5613,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5614,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can define structure for implementing  stack in C\nBy list we can implement stack\nby linked list we can implement stack\n,2.5
-5615,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5616,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented using two data structures: \n1) Array\n2) Linked List,2.5
-5617,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"we can use array, Linked List.",2.5
-5618,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Stacks can be implement using array , linked list or many more",2.5
-5619,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5620,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods to implement stack in C are :-\n.Through array \n.Through linked list,2.5
-5621,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented through array as well as link list.,2.5
-5622,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5623,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,methods are as follows-\n1. push() to push the value in stack\n2. pop() to pop out the value from the stack\n3. is empty () to check stack is empty or not \n4. is full ()  to check stack is full or not \n,2.0
-5624,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can use linked list to get the implementation of stack . \nBy making the function that make have the properties of stack using linked list.,2.5
-5625,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"In stack we store the store the data in FIFO form , so if we have put a data first it will be last to come out.",2.5
-5626,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5627,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,methods to implement stack in c are :\n- through array\n- through linked lists\n,2.5
-5628,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"- create array[capacity]\n- maintain variable like size = 0, front = 0;\n- insert at front position and then size++, front++;\n- deletion can be done at one front position, then size--, front--;",2.5
-5629,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5630,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"It is a kind of Linked list in which the head and the tail of the linked is same. There is no different head and tail of it.\nHence, it is known as circular linked list.",2.5
-5631,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"stack can b implemented using a queue, vector ,array values are first stored in the array, queue than push/pull operation is done.",2.5
-5632,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5633,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5634,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Stack can be implemented in C by using the malloc() function , this will be donr by dynamically using an array. AnD A  top pointer will be used for directing to the top element of the stack. The size of the stack can be same as the size of an array. We can enter the elements of the stack by using push() function and a stack follows the LIFO behaviour that is last in and first out.",2.5
-5635,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5636,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Circular linked list is a special linked list in which the tail of the last node of list points to the head of the same list, forming a closed chain. ",2.5
-5637,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented using functions and array data structure.\nmethods: \n,2.5
-5638,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"We can implement stack using array or linked list. We will have to make the functions to add, pop and find the top element in the stack.",2.0
-5639,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented using arrays or linked lists.,2.5
-5640,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We can use STL using vector to implement stacks in C/C++.\nWe can also use arrays and linked lists and implement stacks with the help of different friend functions.,2.5
-5641,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5642,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"pop(), insert(), top(): built-in methods.\nWithout STL, an array can be used where elements are inserted from the front and the rest are shifted to the right, elements are deleted from the front and the rest are shifted to left.\n",2.0
-5643,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5644,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1) we make an array.\n2) we start placing the element in it that is start taking the initial index of an array.\n3) the first inserted element will come out of the stack in last.\n,2.5
-5645,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"methods to implement stack in C are:-\nimplement stack using array, and its functions such as s.top(),s.push,etc can be perfomed using array by defining different functions .",2.5
-5646,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,To implement stacks in the ,1.0
-5647,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5648,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5649,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,methods to implement stack are:\n,1.0
-5650,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,First we have to implement a header file #include<stack>\nthem can iteratively implement stack or through STL we can.\nStack performs LIFO operation . LAST IN FIRST OUT.\nWe can make the stack and then insert the elements in order.,1.0
-5651,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5652,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Stacks can be implemented using array or linked lists. We can construct our own functions for performing the various operations on them like insertion, deletion, etc.\nor we can also use the built in functions using stl libraries by including them.",2.5
-5653,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"In C, stack can be implemented using a class. We can make a class which stores data in form of array or linked list. We can make our own functions for the insertion, deletion and searching in the class. Insertion with take place at the end node. Deletion will take place by removing the first element and then shifting others. And searching can be done by using any of the relevant search algorithms.",2.5
-5654,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"To implement a stack in c we can use an array or linked list and define insertion ,view and delete operations. we can store the index of the latest entry and upon insertion insert value at index+1 , or while retrieval access the maximum index element as it would be the latest or while deletion free the space occupied by the latest index.",2.5
-5655,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,by using the appropriate declaration.,2.5
-5656,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,A circular linked list is one in which the last element is linked to the first element (like a snake biting its own tail),2.5
-5657,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,There are different methods to implement stacks in C like;\n(i)Using STL\n(ii)Using Vectors,1.0
-5658,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods to implement stack in C is by using the inbuilt stack library of STL called <stack> as the header file and then use the inbuilt functions called push pop and top.,1.0
-5659,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods to implement stack in C are:\n1) Array\n2) Built in STL library <stack>,2.5
-5660,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,One of the method to implement stack in C is by using a double dimension array (2D - array).,2.5
-5661,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We can use an array to implement stack in C.,2.0
-5662,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented using arrays.,2.0
-5663,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented using push and pop operations in C. Push is used to add elements in the stack whereas pop is used to remove or delete elements from stack. Ex: push(4) etc,2.5
-5664,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,There are two methods to implement stack in c:\n1. using array\n2. using linked list,2.5
-5665,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"There are several ways to implement a stack in C, including:\n\n1. Using an array\n\n2. Using a linked list\n\n3. Using a dynamic array\n\n4. Using the standard library",2.5
-5666,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We can use struct for implementing stack in c or simply use and array the same as in cpp and enforce the functions for the array throughout the code to follow the functions for the stack.,2.5
-5667,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implement in C using two queues and STL,2.5
-5668,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,WE can use an array and assign function to it such that in takes and deletes data in a manner similar to stack.,2.0
-5669,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can use stack function or it can be implemented with the help of 2 queues an many more,2.0
-5670,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"To implement stack in C, we can form a struct and then implementing each operation to it.",1.0
-5671,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Arrays, vectors, linked list can be used to implement stack. It would have insert/push, delete/pop, top functions",2.5
-5672,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1)Linked list\n2)array,2.5
-5673,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,using array or using linked list \n,2.5
-5674,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Vector \n,1.0
-5675,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,by creating function to implement stack in c.,2.5
-5676,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,a stack can be implemented either by using a array or by using a linked list,2.5
-5677,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"push, pop, isempty, top",1.0
-5678,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"stack in C can be implemented in many ways, using arrays is the easiest one",2.5
-5679,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,LIFO(Last in first out),1.0
-5680,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,lLIFO METHOD IS USED TO IMPLEMENT THE STACK IN C AND THEE OPERATIONS IS INSERTION UPDATION AND DELETION.,2.5
-5681,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,it can be implemented using  array or linked list.,2.5
-5682,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Stacks can be implemented using arrays, linked list or STL in C.",2.5
-5683,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented in C using STL as well as array.,2.5
-5684,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5685,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented using \n1.array\n2. linked list \n,2.5
-5686,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,push : Adds an element to the top of the stack.\npop : Removes the topmost element from the stack.\nisEmpty : Checks whether the stack is empty.\nisFull : Checks whether the stack is full.\ntop : Displays the topmost element of the stack.,2.5
-5687,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can implement stack using arrays and can push values in the array.,2.5
-5688,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Linked list and vectors help to implement stack in C.,2.5
-5689,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,there is a stack header available in std library otherwise a simple stack can also be implemented using an array.,2.5
-5690,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"methods to implement stack in c is by\n1 importing the stack class from the package \n2, defining the stack functionality by array and making methods to perform the operations each time maintaining the stack LIFO property",2.5
-5691,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods are:\n1. Push(): Insert a new element into the stack i.e just insert a new element at the beginning of the linked list.\n2. Pop(): Return the top element of the Stack i.e simply delete the first element from the linked list.\n3. Peek(): Return the top element.\n4. Display(): Print all elements in Stack.,2.5
-5692,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"we can implement stacks using queue,",2.5
-5693,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,FOR implementing the stack we use last in first out so that we can searchg out with depth wise way ,2.5
-5694,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1. Use the Standard Template Library\n2. To create a Class in which the methods perform the functions of the stack,1.0
-5695,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"#include<stack>\npush(),pop(),top(),",2.5
-5696,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods to implement stack in C are:\n,1.0
-5697,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,methods to implement stack are array and linked list,2.5
-5698,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,s.push()     s. pop()      ,2.5
-5699,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1)using arrays\n2)using linked lists,2.5
-5700,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Array,2.5
-5701,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Push , Pop, are the methods to implement stack in C .",2.5
-5702,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,push- Add an element to the top of stack.\npop- Remove the topmost element from stack.\ntop- display the topmost element.,2.5
-5703,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can implement stack using STL and array.,2.5
-5704,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Array \nSTL,2.0
-5705,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,The way we can implement stack in C are by using vectors and by implementing each function for Stack to be performed. ,1.0
-5706,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"array, STL",2.0
-5707,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5708,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented using:-\n1. Arrays\n2. Linked lists,2.5
-5709,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented using linked list,2.5
-5710,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Circular list is a list in which the last node contains the address of the first node or head node. ,2.5
-5711,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,In circular linked list last node is connected with start node,2.5
-5712,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5713,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"using linked list we can implement stacks, creating a structured class and make node calling parameters\nto implement stacks",1.0
-5714,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,->by array\n->by Stack,1.0
-5715,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5716,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,The methods to implement stack in C are:\n1- push\n2- pop\n3- isEmpty\n4- isFull\n5- top,2.0
-5717,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5718,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,push\npop\nisEmpty\nisFull \ntop ,2.0
-5719,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Inbuilt libraries can be used to implement stack. \n#include<c.stack>\nCreate array where you empty out first index element of array.\n,1.0
-5720,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5721,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented in C using array,1.0
-5722,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1)Using array\n2)Using node,2.0
-5723,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Methods to implement stack in C are: make a structure and define the functions which we want to implement on the stack. eg sort, push, pop etc. and then use them.",2.0
-5724,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5725,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Using inbuilt libraries is one of the efficient ways to implement stack in C. Linked List/Arrays can be another efficient means to create stacks if we don't wish to use inbuilt libraries.,1.0
-5726,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Linked list in which the last node is refering to its first node,2.0
-5727,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,an array can be declared with capacity C. we maintain top variable to look for the top element. limit is checked if number of the elements in the array crosses C.,1.0
-5728,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"stack can be implemented by array or linked list. the operations used will be push, pop, search etc.",1.0
-5729,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented using-\narrays\nlinked lists\nheader file of stack class,1.0
-5730,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,In circular linked list the first node is connected to the tail node due to which each node is connected to its next node which forms a circle.,2.0
-5731,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Using STL or creating your own class.,1.0
-5732,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,push() \npop() \ntop()\nisEmpty()\nsize(),2.0
-5733,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Stack can be implemented by three methods:\n\n1. Using STL\n2. Using an array - We make a class 'node' and initialize an array, data and a top element index which is initially -1. Then using constructor, we take values in data and dynamically make an array. Then we write functions for finding the top element, popping out the element, finding the size etc.\n3. Using Linked List - Same procedure as above but rather an array we make a linked list.",2.0
-5734,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented by making a class of it or can directly use the library # include<stack>,2.0
-5735,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Using 2-D Array\n,2.0
-5736,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5737,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"we can perform basic mathematical operations directly on ADT, like + - * /\nalso they act as a basic building block for the advanced data structures.",2.0
-5738,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5739,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5740,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can use STL or define struct or our own class and write our own code for generating and implementing a stack.,1.0
-5741,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"To implement stack we need to create  function like top ,insert,  delete and create array on which we can apply these functions",1.0
-5742,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,There are two methods to implement stack by array :-\n1) FILO - First element is the the last to be popped out\n2) LIFO - Last element is the first to be popped out,1.0
-5743,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,A circular linked list is the data structure in which the tail of the list contains the address of the head or there is no head or tail.,2.0
-5744,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,\n,1.0
-5745,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"stacks can be implemented using stl vectors or arrays and also linked lists and using the functions like top,pop, push ton store and input data in them.",1.0
-5746,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"recursive function, infinix to postfix",1.0
-5747,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5748,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented in C using an array where the first empty index of the array is stored and elements are inserted at that index and then the index is incremented. When popping we can decrement that stored index so that the value to be deleted will be overwritten by the next pushed element.,2.0
-5749,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,push and pop,1.0
-5750,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,The method to implement stack are \npush()\npop()\nisEmpty(),1.0
-5751,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack is implemented using array or linked list in C.,2.0
-5752,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5753,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,in circular linked list the first element is connected to the last element as well thus making a circle or a ring.,1.0
-5754,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stl that is standard template libraries,1.0
-5755,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"using arrays , vectors or linked list stack can be implemented in C . \n",1.0
-5756,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"We can use an struct, create an array and define all the functions that we will be needing like push, pop, etc",2.0
-5757,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,
-5758,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We push and pull elements in stack to create a stack like the name of stack is s then s.push() and s.pull()..,2.0
-5759,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1. linked list\n2. array\n3. STL(standard template library),2.0
-5760,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,circular linked list refers to linked list whose last node's address is stored in first node.,2.0
-5761,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,two methods can be used: \none -> array\ntwo-> list,2.0
-5762,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack can be implemented using arrays. ,2.0
-5763,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5764,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,recursion can be used as it uses stack. \nAlso array can be used just we have to keep in mind that only the top of it can be accessed at any point.,1.0
-5765,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented to carry out arthimatic operations or to decide the order of certain things.,1.0
-5766,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"We can implement the stack by creating different function for push, pop, insert, delete, top using the aray or linked list data structure.",2.0
-5767,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1) Linked List\n2) Vectors,1.0
-5768,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"To implement stack in C , we can use struct and implement stack ourselves by defining its functions\nor we can use inbuilt STL and include stack.",1.0
-5769,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can implement stack in c by pop or push function in the stack.,1.0
-5770,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods to implement stack in C,1.0
-5771,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,A circular linked list is a linked list in which the \,1.0
-5772,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"To implement stack in C, we can use stuck or class to create Stack class and have two data members one to store data and the other to point at the next data value.",1.0
-5773,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack in c is implemented with  LIFO (LAST IN FIRST OUT) method ,1.0
-5774,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"Methods to implement stack are using arrays, using queue, and double ended queue. ",1.0
-5775,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,To implement stack we can either use stl and use stack<> vector \nor we can create a pop function with the following method\nvoid pop(int x)\n{\nif(p== st.length()-1)\nreturn;\nelse\nst[p]=x;\n},1.0
-5776,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented in c using array where the first empty index of the array is stored and element are inserted at their indexes ,2.0
-5777,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Stack follows last in first out rule so first we push element into it and on the retrieval we pop the element and that element which was added in the end gets retrieve first.,1.0
-5778,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"There are no predefined library for Stacks in C, that's why there are some ways by which can be implemented using array, by linked list",1.0
-5779,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,By using push function we can implement stack in c,1.0
-5780,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we can implement stack in C using arrays and  pointers ,1.0
-5781,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,methods to implement stack in c are:\na) by creating and using library\nb)by creating array and then storing it in the form of stack\nc)by using vectors ,1.0
-5782,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5783,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5784,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5785,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"methods to implement stack are:\ninsertion,deletion,isEmpty,size,etc.",1.0
-5786,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,you can implement a stack using and linked list ,1.0
-5787,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"inserion, deletion, sorting.",0.0
-5788,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Linked list\nusing two queues \nusing predefined libraries\n\n,1.0
-5789,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,There are 2 ways to implement stack.\n1) Using Vector or An array with the condition that the first input in should be the last input out.\n2) Using linked list . ,1.0
-5790,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,using array or by using linked list,1.0
-5791,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5792,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"we Can implement stack in C using arrays , linked list etc.",1.0
-5793,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5794,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,array and linkedlist.,1.0
-5795,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Method 1: (Stack Implementation in C using Array) .\nMethod 2: (Stack Implementation using Linked List). \n,1.0
-5796,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"using array,linkedlist,or using queue",1.0
-5797,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Mainly Two methods are used two implement stack in c that is\narray implementation\nLinked list implementation\n,1.0
-5798,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"methods of implement stack in C  is array ,linked list, queue",1.0
-5799,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,List ADT is type of structure which stores data in a linear way such that bits can be accessed accordingly ,1.0
-5800,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"push,pop,top,isEmpty are the methods",0.0
-5801,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"1. Declare a stack with the correct syntax and name.\n2. push the data into the stack.\n3. perform the required operation.\n4. when complete, you can delete the stack. ",1.0
-5802,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods to implement the stack in C: \n1) Using linked list\n2) Using Array,1.0
-5803,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1.Making structure for stack;\n2.Uisng array and fixing insertion and deletion operations.,1.0
-5804,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Methods to implement stack in C:\nUsing linked list\nUsing array,1.0
-5805,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Define a structure for stack and use array to make it.,1.0
-5806,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based, pointer of the last node does connects to the first node instead of connecting to NULL.\nA doubly linked list whose \,1.0
-5807,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,in c stack can be implemented using linked list,1.0
-5808,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,methods:-\nSTL\npop and push,1.0
-5809,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,obtained from primitive data type.,0.0
-5810,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,it can be implemented using linked list in which when we push the element it is connected to the end and when we pop the element the end element is deleted.\nit can also be implemented by using dynamic array.,1.0
-5811,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,A stack in C can be implemented by creating a linked list where the first node is the top node of the stack. It stores address of the next node.,1.0
-5812,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"we can implement stack using a linked list using a pointer which stores the address of the data inserted most recently, which will help to use it to perform all the operations like insertion/deletion must be done on that element(element inserted most recently). or we can implement stack using a array using a pointer which stores the address of the data inserted most recently.",2.0
-5813,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5814,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,To implement stack in  c first we have to make a dynamic array and make a integer variable .In between filling array add one in variable .if we have to remove an element then we will remove the element on location of variable and minus one in variable.,2.0
-5815,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5816,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"we can use STL, or an array to work as a stack by assigning value to a variable referring the top of the stack and changing it accordingly",1.0
-5817,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We store data in the stack and pops out its element according to our need.,0.0
-5818,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be implemented either through array or through linked list,2.5
-5819,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5820,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,methods to implement stack in c:,0.0
-5821,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5822,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,Using STL: stack<int>st;\n\nOr doing it manualy by creating a push (a function to insert a data) pop (function to remove data) function for an array.,1.0
-5823,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"The methods to implement stack in c are by making a push function that can push data , pop function which can remove a data, is full function whicjh can check whether the stack is full or not and is empty which can check whetger the stack is empty or not.",1.0
-5824,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5825,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,array \nlinked list,2.5
-5826,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5827,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We maintain a next-Index pointer over the array(stack) to insert or delete elements. Whenever next-Index moves out of range it moves back to start or end if some position are left to fill in the array to optimize space. These extra spaces are left due to pop operation performed.,1.0
-5828,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5829,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1. Linked list\n2.Arrays,2.5
-5830,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack can be  implemented using a list ,2.0
-5831,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5832,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,make a stack like we used to make class \nto insert the element write push( )\nand to remove the element pop( ),0.0
-5833,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,1;- Using array.\n2:- Using linked list\n3;-  Using Queue,2.5
-5834,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,to implement stack in C language first we need to push the elements into the stack and then pop the elements one by one until the stack is empty.,1.0
-5835,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,method to implement stack by linked list  by top to bottom approach.,2.0
-5836,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5837,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We can implement stack in C in the form of array and linked list both.,2.5
-5838,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack is implemented  by FIFO method that is first in first out which means that element is which is inserted first will be coming out first,1.0
-5839,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5840,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5841,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,either using stack [ ] or int<stack>stack_name int<vector>stack_name,1.0
-5842,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5843,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,we first import required libraries for stack command and then create an array and ,0.0
-5844,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,element added by first come basis then push pop functioins are implemented accordingly,0.0
-5845,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5846,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,We can implement stack in C using Arrays and structs in C,0.0
-5847,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,,0.0
-5848,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,push pop\nis empty\nis full,0.0
-5849,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,insertion \ndeletion ,0.0
-5850,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,#include <stack.h>\nstack <int> q;\npush(q) = 1;,0.0
-5851,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,for implementation of stack in c we have to import the library and then  create a valuable for the stack and give that  a valid and then out stack is implement in c,0.0
-5852,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,In the form of \n1) array and \n2) linked list,2.5
-5853,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stating of linked list connect to end of linked list it form like circular,0.0
-5854,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,queue.,0.0
-5855,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,stack could be used for many applications like in deletion and insertion in linked list ,0.0
-5856,What are the methods to implement stack in C?,The methods to implement stacks are: (1)  Array based (2) Linked list based,"methods to implement stack in c is bucket sort, ",0.0