4909789
On Pancyclic Claw-Free Graphs
30829488
Vertex pancyclism in claw-free graphs
Abstract In this paper, we show four sufficient conditions for vertex pancyclicity in claw-free graphs. These results improve, respectively, results of Broersma and Veldman, Gould and Jacobson, and Ryjacek. In particular, we prove that if G is a 3-connected claw-free graph such that the vertices of degree 1 of every induced bull have a common neighbor in G then every vertex of G is contained in a cycle of all possible lengths except for four and five. We conjecture that almost all pancyclic claw-free graphs with minimum degree at least three are vertex pancyclic.
41518374
Longest cycles in regular 2-connected claw-free graphs
In this paper, we show that every 2-connected, k-regular claw-free graph on n vertices contains a cycle of length at least min 4k?2, n (k?8), and this result is best possible.
29060290
Hamiltonian Cycles in N2-Locally Connected Claw-Free Graphs
5906681
Circumferences of 2-Connected Claw-Free Graphs
879315
Kolmogorov Random Graphs and the Incompressibility Method
We investigate topological, combinatorial, statistical, and enumeration properties of finite graphs with high Kolmogorov complexity (almost all graphs) using the novel incompressibility method. Example results are (i) the mean and variance of the number of (possibly overlapping) ordered labeled subgraphs of a labeled graph as a function of its randomness deficiency (how far it falls short of the maximum possible Kolmogorov complexity) and (ii) a new elementary proof for the number of unlabeled graphs.
979117
The software architecture of a distributed problem-solving environment
This paper describes the functionality and software architecture of a generic problem-solving environment (PSE) for collaborative computational science and engineering. A PSE is designed to provide transparent access to heterogeneous distributed computing resources, and is intended to enhance research productivity by making it easier to construct, run, and analyze the results of computer simulations. Although implementation details are not discussed in depth, the role of software technologies such as CORBA, Java, and XML is outlined. An XML-based component model is presented. The main features of a Visual Component Composition Environment for software development, and an Intelligent Resource Management System for scheduling components, are described. Some prototype implementations of PSE applications are also presented. Copyright 2000 John Wiley & Sons, Ltd.
14168735
Inductive Reasoning and Kolmogorov Complexity
Abstract Reasoning to obtain the “truth” about reality, from external data, is an important, controversial, and complicated issue in man's effort to understand nature. (Yet, today, we try to make machines do this.) There have been old useful principles, new exciting models, and intricate theories scattered in vastly different areas including philosophy of science, statistics, computer science, and psychology. We focus on inductive reasoning in correspondence with ideas of R. J. Solomonoff. While his proposals result in perfect procedures, they involve the noncomputable notion of Kolmogorov complexity. In this paper we develop the thesis that Solomonoff's method is fundamental in the sense that many other induction principles can be viewed as particular ways to obtain computable approximations to it. We demonstrate this explicitly in the cases of Gold's paradigm for inductive inference, Rissanen's minimum description length (MDL) principle, Fisher's maximum likelihood principle, and Jaynes' maximum entropy principle. We present several new theorems and derivations to this effect. We also delimit what can be learned and what cannot be learned in terms of Kolmogorov complexity, and we describe an experiment in machine learning of handwritten characters. We also give an application of Kolmogorov complexity in Valiant style learning, where we want to learn a concept probably approximately correct in feasible time and examples.
6593224
DNA sequencing and string learning
In laboratories the majority of large-scale DNA sequencing is done following theshotgun strategy, which is to sequence large amount of relatively short fragments randomly and then heuristically find a shortest common superstring of the fragments [26].We study mathematical frameworks, under plausible assumptions, suitable for massive automated DNA sequencing and for analyzing DNA sequencing algorithms. We model the DNA sequencing problem as learning a string from its randomly drawn substrings. Under certain restrictions, this may be viewed as string learning in Valiant's distribution-free learning model and in this case we give an efficient learning algorithm and a quantitative bound on how many examples suffice.One major obstacle to our approach turns out to be a quite well-known open question on how to approximate a shortest common superstring of a set of strings, raised by a number of authors in the last 10 years [9], [29], [30]. We give the firstprovably good algorithm which approximates a shortest superstring of lengthn by a superstring of lengthO(n logn). The algorithm works equally well even in the presence of negative examples, i.e., when merging of some strings is prohibited.
14049212
The Incompressibility Method
Kolmogorov complexity is a modern notion of randomness dealing with the quantity of information in individual objects; that is, pointwise randomness rather than average randomness as produced by a random source. It was proposed by A.N. Kolmogorov in 1965 to quantify the randomness of individual objects in an objective and absolute manner. This is impossible for classical probability theory. Kolmogorov complexity is known variously as 'algorithmic information', 'algorithmic entropy', 'Kolmogorov-Chaitin complexity', 'descriptional complexity', 'shortest program length', 'algorithmic randomness', and others. Using it, we developed a new mathematical proof technique, now known as the 'incompressibility method'. The incompressibility method is a basic general technique such as the 'pigeon hole' argument, 'the counting method' or the 'probabilistic method'. The new method has been quite successful and we present recent examples. The first example concerns a "static" problem in combinatorial geometry. From among (n 3)triangles with vertices chosen from among n points in the unit square, U, let T be the one with the smallest area, and let A be the area of T. Heilbronn's triangle problem asks for the maximum value assumed by A over all choices of n points. We consider the average-case: If the n points are chosen independently and at random (uniform distribution) then there exist positive c and C such that c/n3 < µn < C/n3 for all large enough n, where µn is the expectation of A. Moreover, c/n3 < A < C/n3 for almost all A, that is, almost all A are close to the expectation value so that we determine the area of the smallest triangle for an arrangement in "general position". Our second example concerns a "dynamic" problem in average-case running time of algorithms. The question of a nontrivial general lower bound (or upper bound) on the average-case complexity of Shellsort has been open for about forty years. We obtain the first such lower bound.