Counting Sampling And Integrating Algorithms And Complexity

The subject of these notes is counting and related topics, viewed from a computational perspective. A major theme of the book is the idea of accumulating information about a set of combinatorial structures by performing a random walk on those structures. These notes will be of value not only to teachers of postgraduate courses on these topics, but also to established researchers. For the first time this body of knowledge has been brought together in a single volume.

Computational Complexity of Counting and Sampling provides readers with comprehensive and detailed coverage of the subject of computational complexity. It is primarily geared toward researchers in enumerative combinatorics, discrete mathematics, and theoretical computer science. The book covers the following topics: Counting and sampling problems that are solvable in polynomial running time, including holographic algorithms; #P-complete counting problems; and approximation algorithms for counting and sampling. First, it opens with the basics, such as the theoretical computer science background and dynamic programming algorithms. Later, the book expands its scope to focus on advanced topics, like stochastic approximations of counting discrete mathematical objects and holographic algorithms. After finishing the book, readers will agree that the subject is well covered, as the book starts with the basics and gradually explores the more complex aspects of the topic. Features: Each chapter includes exercises and solutions Ideally written for researchers and scientists Covers all aspects of the topic, beginning with a solid introduction, before shifting to computational complexity’s more advanced features, with a focus on counting and sampling

This book constitutes the refereed proceedings of the 24th International Symposium on Algorithms and Computation, ISAAC 2013, held in Hong Kong, China in December 2013. The 67 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 177 submissions for inclusion in the book. The focus of the volume in on the following topics: computation geometry, pattern matching, computational complexity, internet and social network algorithms, graph theory and algorithms, scheduling algorithms, fixed-parameter tractable algorithms, algorithms and data structures, algorithmic game theory, approximation algorithms and network algorithms.

This book constitutes the refereed proceedings of the 6th International Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 2002, held in Cambridge, MA, USA in September 2002. The 21 revised full papers presented were carefully reviewed and selected from 48 submissions. Among the topics addressed are coding, geometric computations, graph colorings, random hypergraphs, graph computations, lattice computations, proof systems, probabilistic algorithms, derandomization, constraint satisfaction, and web graphs analysis.

contents: vol 1 : Algorithms; Computational Complexity; Distributed Computing; Natural Computing.

This book is based on columns and tutorials published in the Bulletin of the European Association for Theoretical Computer Science (EATCS) during the period 2000OCo2003. It presents many of the most active current research lines in theoretical computer science. The material appears in two volumes, OC Algorithms and ComplexityOCO and OC Formal Models and SemanticsOCO, reflecting the traditional division of the field. The list of contributors includes many of the well-known researchers in theoretical computer science. Most of the articles are reader-friendly and do not presuppose much knowledge of the area in question. Therefore, the book constitutes very suitable supplementary reading material for various courses and seminars in computer science. Contents: Vol 1: Algorithms; Computational Complexity; Distributed Computing; Natural Computing; Vol 2: Formal Specification; Logic in Computer Science; Concurrency; Formal Language Theory. Readership: Upper level undergraduates, graduate students and researchers in theoretical computer science and biocomputing."

In a probabilistic model, a rare event is an event with a very small probability of occurrence. The forecasting of rare events is a formidable task but is important in many areas. For instance a catastrophic failure in a transport system or in a nuclear power plant, the failure of an information processing system in a bank, or in the communication network of a group of banks, leading to financial losses. Being able to evaluate the probability of rare events is therefore a critical issue. Monte Carlo Methods, the simulation of corresponding models, are used to analyze rare events. This book sets out to present the mathematical tools available for the efficient simulation of rare events. Importance sampling and splitting are presented along with an exposition of how to apply these tools to a variety of fields ranging from performance and dependability evaluation of complex systems, typically in computer science or in telecommunications, to chemical reaction analysis in biology or particle transport in physics. Graduate students, researchers and practitioners who wish to learn and apply rare event simulation techniques will find this book beneficial.