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OverviewComputer science and physics have been closely linked since the birth of modern computing. In recent years, an interdisciplinary area has blossomed at the junction of these fields, connecting insights from statistical physics with basic computational challenges. Researchers have successfully applied techniques from the study of phase transitions to analyze NP-complete problems such as satisfiability and graph coloring. This is leading to a new understanding of the structure of these problems, and of how algorithms perform on them. Computational Complexity and Statistical Physics will serve as a standard reference and pedagogical aid to statistical physics methods in computer science, with a particular focus on phase transitions in combinatorial problems. Addressed to a broad range of readers, the book includes substantial background material along with current research by leading computer scientists, mathematicians, and physicists. It will prepare students and researchers from all of these fields to contribute to this exciting area. Full Product DetailsAuthor: Allon Percus (Institute for Pure & Applied Mathematics, Institute for Pure & Applied Mathematics, UCLA) , Gabriel Istrate (, Los Alamos National Laboratory) , Cristopher Moore (Departments of Computer Science and Astronomy and Physics, Departments of Computer Science and Astronomy and Physics, University of New Mexico)Publisher: Oxford University Press Inc Imprint: Oxford University Press Inc Dimensions: Width: 23.40cm , Height: 2.00cm , Length: 15.60cm Weight: 0.535kg ISBN: 9780195177381ISBN 10: 019517738 Pages: 384 Publication Date: 09 March 2006 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: To order  Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us. Table of ContentsAllon G. Percus, Gabriel Istrate, and Cristopher Moore: Preface Part 1: Fundamentals 1: Allon G. Percus, Gabriel Istrate, and: Introduction: Where Statistical Physics Meets Computation Cristopher Moore 2: Gil Kalai and Shmuel Safra: Threshold Phenomena and Influence: Perspectives from Mathematics, Computer Science, and Economics Part 2: Statistical Physics and Algorithms 3: Simona Cocco, Remi Monasson, Andrea Montanari, and Guilhem Semerjian: Analyzing Search Algorithms with Physical Methods 4: Alfredo Braunstein, Marc Mezard, Martin Weigt, and Riccardo Zecchina: Constraint Satisfaction by Survey Propagation 5: Stephan Mertens: The Easiest Hard Problem: Number Partitioning 6: Sigismund Kobe and Jarek Krawczyk: Ground States, Energy Landscape and Low-Temperature Dynamics of plus/minus Spin Glasses Part 3: Identifying the Threshold 7: Lefteris M. Kirousis, Yannis C. Stamatiou, and Michele Zito: The Satisfiability Threshold Conjecture: Techniques Behind Upper Bound Improvements 8: Alexis C. Kaporis, Lefteris M. Kirousis, and Yannis C. Stamatiou: Proving Conditional Randomness Using the Principle of Deferred Decisions 9: Demetrios D. Demopoulos, and Moshe Y. Vardi: The Phase Transition in the Random HornSAT Problem Part 4: Extensions and Applications 10: Tad Hogg: Phase Transitions for Quantum Search Algorithms 11: Zoltan Toroczkai, Gyorgy Korniss, Mark A. Novotny, and Hasan Guclu: Scalability, Random Surfaces and Synchronized Computing Networks 12: Christian M. Reidys: Combinatorics of Genotype-Phenotype Maps: An RNA Case Study 13: Harry B. Hunt, III, Madhav V. Marathe, Daniel J. Rosenkrantz, and Richard E. Stearns: Towards a Predictive Computational Complexity Theory for Periodically Specified Problems: A Survey Bibliography IndexReviewsThis volume provides a comprehensive overview of an exciting new research area at the interface between statistical physics and computer science. It is an excellent exposition, featuring state-of-the-art contributions by renowned researchers in the field. The book will serve as a useful reference for years to come. Bart Selman, Cornell University<br> Author InformationAllon Percus is Associate Director of the Institute for Pure and Applied Mathematics at UCLA, and a scientist at Los Alamos National Laboratory. He received his Ph.D. in Theoretical Physics from the University of Paris, Orsay, in 1997. His research has combined statistical physics, discrete mathematics, and computer science, focusing primarily on local search algorithms in combinatorial optimization. He has organized numerous conferences and workshops on combinatorics, phase transitions, and algorithmic complexity. Gabriel Istrate is a scientist at Los Alamos National Laboratory, in the Basic and Applied Simulation Science group. He received his Ph.D. in Computer Science from the University of Rochester in 1999. His primary research interests are in combinatorial, game theoretic, and probabilistic aspects of complex systems. His work in the area of phase transitions has focused on the interplay between threshold properties and computational complexity. Cristopher Moore is an Associate Professor at the University of New Mexico, and holds a joint appointment in the Computer Science and Physics departments. He received his Ph.D. in Physics from Cornell University in 1991. He has published 80 papers at the interface between these two fields, on topics ranging from statistical physics and phase transitions to quantum algorithms and mapping the internet. Tab Content 6Author Website:Countries AvailableAll regions |
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