LEADER 04037nam 22006495 450 001 9910299764103321 005 20211101140552.0 010 $a3-319-18138-6 024 7 $a10.1007/978-3-319-18138-7 035 $a(CKB)3710000000412196 035 $a(EBL)2096107 035 $a(SSID)ssj0001501743 035 $a(PQKBManifestationID)11830620 035 $a(PQKBTitleCode)TC0001501743 035 $a(PQKBWorkID)11446993 035 $a(PQKB)10773638 035 $a(DE-He213)978-3-319-18138-7 035 $a(MiAaPQ)EBC2096107 035 $a(PPN)186029128 035 $a(EXLCZ)993710000000412196 100 $a20150505d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStochastic Multi-Stage Optimization $eAt the Crossroads between Discrete Time Stochastic Control and Stochastic Programming /$fby Pierre Carpentier, Jean-Philippe Chancelier, Guy Cohen, Michel De Lara 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (370 p.) 225 1 $aProbability Theory and Stochastic Modelling,$x2199-3130 ;$v75 300 $aDescription based upon print version of record. 311 $a3-319-18137-8 320 $aIncludes bibliographical references and index. 327 $aI Preliminaries -- 1.Issues and Problems in Decision Making under Uncertainty -- 2.Open-Loop Control: The Stochastic Gradient Method -- II Decision under Uncertainty and the Role of Information.- 3.Tools for Information Handling.- 4.Information and Stochastic Optimization Problems.- Optimality Conditions for SOC Problems -- III Discretization and Numerical Methods -- 6.Discretization Methodology for Problems with SIS -- 7.Numerical Algorithms -- IV Convergence Analysis -- 8.Convergence Issues in Stochastic Optimization -- V Advanced Topics -- 9.Multi-Agent Decision Problems -- Dual Effect for Multi-Agent Stochastic I-O Systems -- VI Appendices -- A. Basics in Analysis and Optimization -- B. Basics in Probability -- References -- Index. 330 $aThe focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the related discretization issues. There is a growing need to tackle uncertainty in applications of optimization. For example the massive introduction of renewable energies in power systems challenges traditional ways to manage them. This book lays out basic and advanced tools to handle and numerically solve such problems and thereby is building a bridge between Stochastic Programming and Stochastic Control. It is intended for graduates readers and scholars in optimization or stochastic control, as well as engineers with a background in applied mathematics. 410 0$aProbability Theory and Stochastic Modelling,$x2199-3130 ;$v75 606 $aMathematical optimization 606 $aProbabilities 606 $aContinuous Optimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26030 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 615 0$aMathematical optimization. 615 0$aProbabilities. 615 14$aContinuous Optimization. 615 24$aProbability Theory and Stochastic Processes. 676 $a003.76 700 $aCarpentier$b Pierre$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767402 702 $aChancelier$b Jean-Philippe$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCohen$b Guy$f1971-$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aDe Lara$b Michel$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299764103321 996 $aStochastic Multi-Stage Optimization$92494827 997 $aUNINA