LEADER 03993nam 22006375 450 001 9910298477603321 005 20200919062807.0 010 $a3-662-46214-1 024 7 $a10.1007/978-3-662-46214-0 035 $a(CKB)3710000000359163 035 $a(EBL)1998353 035 $a(OCoLC)903929908 035 $a(SSID)ssj0001451902 035 $a(PQKBManifestationID)11836215 035 $a(PQKBTitleCode)TC0001451902 035 $a(PQKBWorkID)11479647 035 $a(PQKB)11528523 035 $a(DE-He213)978-3-662-46214-0 035 $a(MiAaPQ)EBC1998353 035 $a(PPN)18449463X 035 $a(EXLCZ)993710000000359163 100 $a20150221d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStochastic Optimization Methods $eApplications in Engineering and Operations Research /$fby Kurt Marti 205 $a3rd ed. 2015. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015. 215 $a1 online resource (389 p.) 300 $aDescription based upon print version of record. 311 $a3-662-46213-3 320 $aIncludes bibliographical references and index. 327 $aStochastic Optimization Methods -- Optimal Control Under Stochastic Uncertainty -- Stochastic Optimal Open-Loop Feedback Control -- Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) -- Optimal Design of Regulators -- Expected Total Cost Minimum Design of Plane Frames -- Stochastic Structural Optimization with Quadratic Loss Functions -- Maximum Entropy Techniques. 330 $aThis book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research. 606 $aOperations research 606 $aDecision making 606 $aMathematical optimization 606 $aComputational intelligence 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 615 0$aOperations research. 615 0$aDecision making. 615 0$aMathematical optimization. 615 0$aComputational intelligence. 615 14$aOperations Research/Decision Theory. 615 24$aOptimization. 615 24$aComputational Intelligence. 676 $a519.2 700 $aMarti$b Kurt$4aut$4http://id.loc.gov/vocabulary/relators/aut$0223988 906 $aBOOK 912 $a9910298477603321 996 $aStochastic Optimization Methods$92520033 997 $aUNINA