Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present linear-I/O algorithms for fundamental graph problems on embedded outerplanar graphs.  show that breadth-first search, depth-first search, single-source shortest paths, triangulation, and computing an � separator of size O(1/� )t akeO(scan(N)) I/Os on embedded outerplanar graphs.  also show that it takes O(sort(N)) I/Os to test whether a given graph is outerplanar and to compute an outerplanar embedding of an outerplanar graph, thereby providing O(sort(N))-I/O algorithms for the above problems if no embedding of the graph is given. As all these problems have linear-time algorithms in internal memory, a simple simulation technique can be used to improve the I/O-complexity of our algorithms from O(sort(N)) to O(perm(N)).  prove matching lor bounds for embedding, breadth-first search, depth-first search, and singlesource shortest paths if no embedding is given. Our algorithms for the above problems use a simple linear-I/O time-forward processing algorithm for rooted trees whose vertices are stored in preorder.
['Anil Maheshwari', 'Norbert Zeh']
I am writing a research paper on a idea I/O-optimal algorithms for outerplanar graphs
[{"from": "human", "value": "I am writing a research paper on a idea I/O-optimal algorithms for outerplanar graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present linear-I/O algorithms for fundamental graph problems on embedded outerplanar graphs.  show that breadth-first search, depth-first search, single-source shortest paths, triangulation, and computing an \ufffd separator of size O(1/\ufffd )t akeO(scan(N)) I/Os on embedded outerplanar graphs.  also show that it takes O(sort(N)) I/Os to test whether a given graph is outerplanar and to compute an outerplanar embedding of an outerplanar graph, thereby providing O(sort(N))-I/O algorithms for the above problems if no embedding of the graph is given. As all these problems have linear-time algorithms in internal memory, a simple simulation technique can be used to improve the I/O-complexity of our algorithms from O(sort(N)) to O(perm(N)).  prove matching lor bounds for embedding, breadth-first search, depth-first search, and singlesource shortest paths if no embedding is given. Our algorithms for the above problems use a simple linear-I/O time-forward processing algorithm for rooted trees whose vertices are stored in preorder."}]