LEADER 04576nam 22005655 450 001 9910864198403321 005 20250807132512.0 010 $a9783031400599$b(electronic bk.) 010 $z9783031400582 024 7 $a10.1007/978-3-031-40059-9 035 $a(MiAaPQ)EBC31354965 035 $a(Au-PeEL)EBL31354965 035 $a(CKB)32157537700041 035 $a(DE-He213)978-3-031-40059-9 035 $a(OCoLC)1435877685 035 $a(EXLCZ)9932157537700041 100 $a20240527d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStochastic Optimization Methods $eApplications in Engineering and Operations Research /$fby Kurt Marti 205 $a4th ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (389 pages) 225 0 $aBusiness and Management Series 311 08$aPrint version: Marti, Kurt Stochastic Optimization Methods Cham : Springer International Publishing AG,c2024 9783031400582 327 $aStochastic Optimization Methods -- Solution of Stochastic Linear Programs by Discretization Methods -- Optimal Control under Stochastic Uncertainty -- Random Search Procedures for Global Optimization -- Controlled Random Search under Uncertainty -- Controlled Random Search Procedures for Global Optimization -- Random Search Methods with Multiple Search Points -- Approximation of Feedback Control Systems -- Stochastic Optimal Open-Loop Feedback Control -- Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) -- Machine Learning under stochastic uncertainty -- Stochastic Structural Optimization with quadratic loss functions -- Maximum Entropy Techniques. 330 $aThis book examines optimization problems that in practice involve random model parameters. It outlines the computation of robust optimal solutions, i.e., optimal solutions that are insensitive 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 corresponding deterministic 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. The fourth edition of this classic text has been carefully and thoroughly revised. It includes new chapters on the solution of stochastic linear programs by discretization of the underlying probability distribution, and on solving deterministic optimization problems by means of controlled random search methods and multiple random search procedures. It also presents a new application of stochastic optimization methods to machine learning problems with different loss functions. For the computation of optimal feedback controls under stochastic uncertainty, besides the open-loop feedback procedures, a new method based on Taylor expansions with respect to the gain parameters is presented. The book is intended for researchers and graduate students who are interested in stochastics, stochastic optimization, and control. It will also benefit professionals and practitioners whose work involves technical, economic and/or operations research problems under stochastic uncertainty. 606 $aOperations research 606 $aMathematical optimization 606 $aComputational intelligence 606 $aOperations Research and Decision Theory 606 $aOptimization 606 $aComputational Intelligence 615 0$aOperations research. 615 0$aMathematical optimization. 615 0$aComputational intelligence. 615 14$aOperations Research and Decision Theory. 615 24$aOptimization. 615 24$aComputational Intelligence. 676 $a519.62 700 $aMarti$b Kurt$0223988 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910864198403321 996 $aStochastic Optimization Methods$92520033 997 $aUNINA