Phase Transitions in Combinatorial Optimization Problems

Phase Transitions in Combinatorial Optimization Problems
Author: Alexander K. Hartmann
Publisher: Wiley-VCH
Total Pages: 360
Release: 2005-09-12
Genre: Science
ISBN: 9783527404735

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A concise, comprehensive introduction to the topic of statistical physics of combinatorial optimization, bringing together theoretical concepts and algorithms from computer science with analytical methods from physics. The result bridges the gap between statistical physics and combinatorial optimization, investigating problems taken from theoretical computing, such as the vertex-cover problem, with the concepts and methods of theoretical physics. The authors cover rapid developments and analytical methods that are both extremely complex and spread by word-of-mouth, providing all the necessary basics in required detail. Throughout, the algorithms are shown with examples and calculations, while the proofs are given in a way suitable for graduate students, post-docs, and researchers. Ideal for newcomers to this young, multidisciplinary field.

Phase Transitions in Combinatorial Optimization Problems

Phase Transitions in Combinatorial Optimization Problems
Author: Alexander K. Hartmann
Publisher: John Wiley & Sons
Total Pages: 360
Release: 2006-05-12
Genre: Science
ISBN: 3527606866

Download Phase Transitions in Combinatorial Optimization Problems Book in PDF, Epub and Kindle

A concise, comprehensive introduction to the topic of statistical physics of combinatorial optimization, bringing together theoretical concepts and algorithms from computer science with analytical methods from physics. The result bridges the gap between statistical physics and combinatorial optimization, investigating problems taken from theoretical computing, such as the vertex-cover problem, with the concepts and methods of theoretical physics. The authors cover rapid developments and analytical methods that are both extremely complex and spread by word-of-mouth, providing all the necessary basics in required detail. Throughout, the algorithms are shown with examples and calculations, while the proofs are given in a way suitable for graduate students, post-docs, and researchers. Ideal for newcomers to this young, multidisciplinary field.

Epsilon-transformation

Epsilon-transformation
Author: Weixiong Zhang
Publisher:
Total Pages: 18
Release: 1994
Genre: Combinatorial optimizations
ISBN:

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An Introduction to Metaheuristics for Optimization

An Introduction to Metaheuristics for Optimization
Author: Bastien Chopard
Publisher: Springer
Total Pages: 226
Release: 2018-11-02
Genre: Computers
ISBN: 3319930737

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The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. The authors are experienced, interdisciplinary lecturers and researchers and in their explanations they demonstrate many shared foundational concepts among the key methodologies. This textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics.

Optimization Algorithms in Physics

Optimization Algorithms in Physics
Author: Alexander K. Hartmann
Publisher: Wiley-VCH
Total Pages: 382
Release: 2002-02-25
Genre: Science
ISBN: 9783527403073

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The past few years have witnessed a substantial growth in the number of applications for optimization algorithms in solving problems in the field of physics. Examples include determining the structure of molecules, estimating the parameters of interacting galaxies, the ground states of electronic quantum systems, the behavior of disordered magnetic materials, and phase transitions in combinatorial optimization problems. This book serves as an introduction to the field, while also presenting a complete overview of modern algorithms. The authors begin with the relevant foundations from computer science, graph theory and statistical physics, before moving on to thoroughly explain algorithms - backed by illustrative examples. They include pertinent mathematical transformations, which in turn are used to make the physical problems tractable with methods from combinatorial optimization. Throughout, a number of interesting results are shown for all physical examples. The final chapter provides numerous practical hints on software development, testing programs, and evaluating the results of computer experiments.

Computational Complexity and Statistical Physics

Computational Complexity and Statistical Physics
Author: Allon Percus
Publisher: Oxford University Press, USA
Total Pages: 394
Release: 2006
Genre: Computers
ISBN: 9780195177374

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Computer 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.

New Optimization Algorithms in Physics

New Optimization Algorithms in Physics
Author: Alexander K. Hartmann
Publisher: John Wiley & Sons
Total Pages: 312
Release: 2006-03-06
Genre: Science
ISBN: 352760457X

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Many physicists are not aware of the fact that they can solve their problems by applying optimization algorithms. Since the number of such algorithms is steadily increasing, many new algorithms have not been presented comprehensively until now. This presentation of recently developed algorithms applied in physics, including demonstrations of how they work and related results, aims to encourage their application, and as such the algorithms selected cover concepts and methods from statistical physics to optimization problems emerging in theoretical computer science.

A Guide to Monte Carlo Simulations in Statistical Physics

A Guide to Monte Carlo Simulations in Statistical Physics
Author: David P. Landau
Publisher: Cambridge University Press
Total Pages: 402
Release: 2000-08-17
Genre: Mathematics
ISBN: 9780521653664

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This book describes all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics and statistical mechanics, as well as in related fields, such as polymer science and lattice gauge theory. The authors give a succinct overview of simple sampling methods and develop the importance sampling method. In addition they introduce quantum Monte Carlo methods, aspects of simulations of growth phenomena and other systems far from equilibrium, and the Monte Carlo Renormalization Group approach to critical phenomena. The book includes many applications, examples, and current references, and exercises to help the reader.

Parallel Problem Solving from Nature-PPSN VI

Parallel Problem Solving from Nature-PPSN VI
Author: Marc Schoenauer
Publisher: Springer Science & Business Media
Total Pages: 920
Release: 2000-09-06
Genre: Computers
ISBN: 3540410562

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This book constitutes the refereed proceedings of the 6th International Conference on Parallel Problem Solving from Nature, PPSN VI, held in Paris, France in September 2000. The 87 revised full papers presented together with two invited papers were carefully reviewed and selected from 168 submissions. The presentations are organized in topical sections on analysis and theory of evolutionary algorithms, genetic programming, scheduling, representations and operators, co-evolution, constraint handling techniques, noisy and non-stationary environments, combinatorial optimization, applications, machine learning and classifier systems, new algorithms and metaphors, and multiobjective optimization.