LEADER 05643nam 22007211a 450 001 9910784593203321 005 20200520144314.0 010 $a1-281-05734-7 010 $a9786611057343 010 $a0-08-055059-2 035 $a(CKB)1000000000357675 035 $a(EBL)311314 035 $a(OCoLC)476097735 035 $a(SSID)ssj0000127259 035 $a(PQKBManifestationID)11157323 035 $a(PQKBTitleCode)TC0000127259 035 $a(PQKBWorkID)10052063 035 $a(PQKB)10939319 035 $a(Au-PeEL)EBL311314 035 $a(CaPaEBR)ebr10190045 035 $a(CaONFJC)MIL105734 035 $a(MiAaPQ)EBC311314 035 $a(PPN)140429298 035 $a(EXLCZ)991000000000357675 100 $a20080304d2007 my 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComplex systems$b[electronic resource] $eEcole d'ete de Physique des Houches, session LXXXV, 3-28 July 2006 ; Ecole thematique du CNRS /$fedited by Jean-Phillippe Bouchaud, Marc Mezard and Jean Dalibard 205 $a1st ed. 210 $aBoston, MA $cElsevier$d2007 215 $a1 online resource (527 p.) 225 1 $aLes Houches 300 $aDescription based upon print version of record. 311 $a0-444-53006-1 320 $aIncludes bibliographical references. 327 $aFront cover; Complex Systems; Copyright page; Previous sessions; Organizers; Lecturers; Seminar Speakers; Participants; Auditors; Preface; Contents; Course 1. Introduction to phase transitions in random optimization problems; 1. Introduction; 2. Basic concepts: overview of static phase transitions in K-XORSAT; 3. Advanced methods (I): replicas; 4. Advanced methods (II): cavity; 5. Dynamical phase transitions and search algorithms; 6. Conclusions; Appendix A. A primer on large deviations; Appendix B. Inequalities of first and second moments 327 $aAppendix C. Corrections to the saddle-point calculation of References; Course 2. Modern coding theory: the statistical mechanics and computer science point of view; 1. Introduction and outline; 2. Background: the channel coding problem; 3. Sparse graph codes; 4. The decoding problem for sparse graph codes; 5. Belief Propagation beyond coding theory; 6. Belief Propagation beyond the binary symmetric channel; 7. Open problems; Appendix A. A generating function calculation; References; Course 3. Mean field theory of spin glasses: statics and dynamics; 1. Introduction 327 $a2. General considerations3. Mean field theory; 4. Many equilibrium states; 5. The explicit solution of the Sherrington Kirkpatrick model; 6. Bethe lattices; 7. Finite dimensions; 8. Some other applications; 9. Conclusions; References; Course 4. Random matrices, the Ulam Problem, directed polymers & growth models, and sequence matching; 1. Introduction; 2. Random matrices: the Tracy-Widom distribution for the largest eigenvalue; 3. The longest common subsequence problem (or the Ulam problem); 4. Directed polymers and growth models; 5. Sequence matching problem; 6. Conclusion; References 327 $aCourse 5. Economies with interacting agents1. Introduction; 2. Models of segregation: a physical analogy; 3. Market relations; 4. Financial markets; 5. Contributions to public goods; 6. Conclusion; References; Course 6. Crackling noise and avalanches: scaling, critical phenomena, and the renormalization group; 1. Preamble; 2. What is crackling noise?; 3. Hysteresis and Barkhausen noise in magnets; 4. Why crackling noise?; 5. Self-similarity and its consequences; References; Course 7. Bootstrap and jamming percolation; 1. Introduction; 2. Bootstrap Percolation (BP); 3. Jamming Percolation (JP) 327 $a4. Related stochastic modelsReferences; Course 8. Complex networks; 1. Introduction; 2. Network expansion and the small-world effect; 3. Degree distributions; 4. Further directions; References; Course 9. Minority games; 1. Introduction; 2. The minority game: definition and numerical simulations; 3. Exact solutions; 4. Application and extensions; 5. Conclusions; References; Course 10. Metastable states in glassy systems; 1. Introduction; 2. Mean-field Spin Glasses; 3. The complexity; 4. Supersymmetry breaking and structure of the states; 5. Models in finite dimension; 6. Conclusion; References 327 $aCourse 11. Evolutionary dynamics 330 $aThere has been recently some interdisciplinary convergence on a number of precise topics which can be considered as prototypes of complex systems. This convergence is best appreciated at the level of the techniques needed to deal with these systems, which include: 1) A domain of research around a multiple point where statistical physics, information theory, algorithmic computer science, and more theoretical (probabilistic) computer science meet: this covers some aspects of error correcting codes, stochastic optimization algorithms, typical case complexity and phase transitions, constr 410 0$aLes Houches 606 $aSystem analysis$vCongresses 606 $aComputational complexity$vCongresses 615 0$aSystem analysis 615 0$aComputational complexity 676 $a003 676 $a003.7 701 $aBouchaud$b Jean-Philippe$f1962-$0629192 701 $aMezard$b Mard$01517913 701 $aDalibard$b J$053364 712 12$aEcole d'âetâe de physique thâeorique (Les Houches, Haute-Savoie, France) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784593203321 996 $aComplex systems$93755168 997 $aUNINA