Readership: Graduate students, researchers, and lecturers in statistical physics, information theory, and theoretical computer science.
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
"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
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
