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Phase transitions in combinatorial optimization problems : basics, algorithms and statistical mechanics

Author: Alexander K Hartmann; Martin Weigt; Wiley InterScience (Online service)
Publisher: Weinheim : Wiley-VCH, 2005.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Hartmann, Alexander K.
Phase transitions in combinatorial optimization problems.
Weinheim : Wiley-VCH, 2005
(DLC) 2006482033
(OCoLC)56963714
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Alexander K Hartmann; Martin Weigt; Wiley InterScience (Online service)
ISBN: 3527606734 9783527606733 3527606866 9783527606863 3527404732 9783527404735 1280854006 9781280854002
OCLC Number: 85784937
Description: 1 online resource (xi, 348 pages) : illustrations
Contents: Phase Transitions in Combinatorial Optimization Problems Basics, Algorithms and Statistical Mechanics; Contents; Preface; 1 Introduction; 2 Algorithms; 3 Introduction to graphs; 4 Introduction to complexity theory; 5 Statistical mechanics of the Ising model; 6 Algorithms and numerical results for vertex covers; 7 Statistical mechanics of vertex covers on a random graph; 8 The dynamics of vertex-cover algorithms; 9 Towards new, statistical-mechanics motivated algorithms; 10 The satisfiability problem; 11 Optimization problems in physics; Index.
Responsibility: Alexander K. Hartmann and Martin Weigt.
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Abstract:

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.

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