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Information, physics, and computation / Marc Mézard,... Andrea Montanari,...

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Auteur principal: Mézard, Marc, 1957-...., AuteurCo-auteur: Montanari, Andrea, 1975-...., physicien, AuteurLangue : anglaisPays : Royaume-Uni.Publication : Oxford : Oxford University PressDate du copyright : 2009Description: 1 vol. (XIII-569 p.), ill., couv. ill. en coul., 26 cmISBN : 9780198570837.Collection: Oxford graduate textsNote de contenu : Contient des exercices Résumé : La 4e de couverture porte : "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, adopting 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.".Bibliographie : Bibliogr. p. [547]-564. Index.Sujet - Nom commun: Physique statistique | Codage | Informatique

Autres tirages : 2010, 2012, 2013, 2015, 2016, 2017, 2019

Bibliogr. p. [547]-564. Index

Contient des exercices

La 4e de couverture porte : "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, adopting 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."