LEADER 04904nam 2200697Ia 450 001 9910455223503321 005 20200520144314.0 010 $a1-282-25928-8 010 $a9786612259289 010 $a1-4008-3105-9 024 7 $a10.1515/9781400831050 035 $a(CKB)1000000000788528 035 $a(EBL)457706 035 $a(OCoLC)439040007 035 $a(SSID)ssj0000239025 035 $a(PQKBManifestationID)11220773 035 $a(PQKBTitleCode)TC0000239025 035 $a(PQKBWorkID)10235249 035 $a(PQKB)11591388 035 $a(MiAaPQ)EBC457706 035 $a(DE-B1597)447001 035 $a(OCoLC)979757917 035 $a(DE-B1597)9781400831050 035 $a(PPN)170242854 035 $a(Au-PeEL)EBL457706 035 $a(CaPaEBR)ebr10326354 035 $a(CaONFJC)MIL225928 035 $a(EXLCZ)991000000000788528 100 $a20090414d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRobust optimization$b[electronic resource] /$fAharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski 205 $aCourse Book 210 $aPrinceton, NJ $cPrinceton University Press$dc2009 215 $a1 online resource (565 p.) 225 0 $aPrinceton Series in Applied Mathematics ;$v28 300 $aDescription based upon print version of record. 311 $a0-691-14368-4 327 $t Frontmatter -- $tContents -- $tPreface -- $tPart I. Robust Linear Optimization -- $tChapter One. Uncertain Linear Optimization Problems and their Robust Counterparts -- $tChapter Two. Robust Counterpart Approximations of Scalar Chance Constraints -- $tChapter Three. Globalized Robust Counterparts of Uncertain LO Problems -- $tChapter Four. More on Safe Tractable Approximations of Scalar Chance Constraints -- $tPart II. Robust Conic Optimization -- $tChapter Five. Uncertain Conic Optimization: The Concepts -- $tChapter Six. Uncertain Conic Quadratic Problems with Tractable RCs -- $tChapter Seven. Approximating RCs of Uncertain Conic Quadratic Problems -- $tChapter Eight. Uncertain Semidefinite Problems with Tractable RCs -- $tChapter Nine. Approximating RCs of Uncertain Semidefinite Problems -- $tChapter Ten. Approximating Chance Constrained CQIs and LMIs -- $tChapter Eleven. Globalized Robust Counterparts of Uncertain Conic Problems -- $tChapter Twelve. Robust Classi¯cation and Estimation -- $tPart III. Robust Multi-Stage Optimization -- $tChapter Thirteen. Robust Markov Decision Processes -- $tChapter Fourteen. Robust Adjustable Multistage Optimization -- $tPart IV. Selected Applications -- $tChapter Fifteen. Selected Applications -- $tAppendix A: Notation and Prerequisites -- $tAppendix B: Some Auxiliary Proofs -- $tAppendix C: Solutions to Selected Exercises -- $tBibliography -- $tIndex 330 $aRobust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. 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