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Information, Physics, and Computation
Marc Mézard and Andrea Montanari
584 pages
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140 line drawings
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246x171mm
978-0-19-857083-7
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Hardback
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22 January 2009
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- Landmark book in a new field
- Defines a common set of concepts and common language for three disciplines which used to work independently
- Self-contained, precise. Numerous examples and exercises make it a valuable teaching book
- Builds a bridge between physics of glasses and computer science problems
- Unique place to learn about the cavity method and its connections to message passing algorithms
This book presents a unified approach to a rich and rapidly evolving research domain at the interface between statistical physics, theoretical computer science/discrete mathematics, and coding/information theory. It is accessible to graduate students and researchers without a specific training in any of these fields. The selected topics include spin glasses, error correcting codes, satisfiability, and are central to each field. The approach focuses on large random instances and adopts a common probabilistic formulation in terms of graphical models. It presents message passing algorithms like belief propagation and survey
propagation, and their use in decoding and constraint satisfaction solving. It also explains analysis techniques like density evolution and the cavity method, and uses them to study phase transitions.Readership: Graduate students, researchers, and lecturers in statistical physics, information theory, and theoretical computer science.
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Marc Mézard, Laboratoire de Physique Théorique et Modeles Statistiques, Université de Paris Sud, Orsay, France, and Andrea Montanari, Electrical Engineering and Statistics Department, Stanford University, USA
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"This book is an excellent graduate-level text on the amazing connections between modern error-correcting codes (information theory), spin glass systems (condensed matter physics), and satisfiability problems (computational complexity). [...] I would expect any researcher working near the intersection of information theory, statistical physics and combinatorial optimization to find this book to be a highly-valued resource." - Mathematical Reviews "Information, Physics, and Computation is self-contained and should be accessible to any graduate student with a good background in probability theory and analysis. [] Information, Physics, and Computation stimulates that cross-disciplinary dialog, which is always desirable because from
it, new perspectives emerge." - Physics Today
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1: Introduction to Information Theory
2: Statistical physics and probability theory
3: Introduction to combinatorial optimization
4: Probabilistic toolbox
5: The Random Energy Model
6: Random Code Ensemble
7: Number partitioning
8: Introduction to replica theory
9: Factor graphs and graph ensembles
10: Satisfiability
11: Low-Density Parity-Check Codes
12: Spin glasses
13: Bridges: Inference and Monte Carlo
14: Belief propagation
15: Decoding with belief propagation
16: The assignment problem
17: Ising models on random graphs
18: Linear Boolean equations
19: The 1RSB cavity method
20: Random K-satisfiability
21: Glassy states in coding theory
22: An ongoing story
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The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
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