LEADER 04247nam 22007695 450 001 9910847588003321 005 20240404140544.0 010 $a3-031-52464-0 024 7 $a10.1007/978-3-031-52464-6 035 $a(CKB)31367636200041 035 $a(MiAaPQ)EBC31267137 035 $a(Au-PeEL)EBL31267137 035 $a(MiAaPQ)EBC31252772 035 $a(Au-PeEL)EBL31252772 035 $a(DE-He213)978-3-031-52464-6 035 $a(EXLCZ)9931367636200041 100 $a20240404d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Stochastic Programming$b[electronic resource] $eModels, Algorithms, and Implementation /$fby Lewis Ntaimo 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (518 pages) 225 1 $aSpringer Optimization and Its Applications,$x1931-6836 ;$v774 311 $a3-031-52462-4 327 $a1. Introduction -- 2 Stochastic Programming Models -- 3 Modeling and Illustrative Numerical Examples -- 4 Example Applications of Stochastic Programming -- 5 Deterministic Large-Scale Decomposition Methods -- 6 Risk-Neutral Stochastic Linear Programming Methods -- 7 Mean-Risk Stochastic Linear Programming Methods -- 8 Sampling-Based Stochastic Linear Programming Methods -- 9 Stochastic Mixed-Integer Programming Methods -- 10 Computational Experimentation. . 330 $aThis book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation. The purpose of this book is to provide a foundational and thorough treatment of the subject with a focus on models and algorithms and their computer implementation. The book?s most important features include a focus on both risk-neutral and risk-averse models, a variety of real-life example applications of stochastic programming, decomposition algorithms, detailed illustrative numerical examples of the models and algorithms, and an emphasis on computational experimentation. With a focus on both theory and implementation of the models and algorithms for solving practical optimization problems, this monograph is suitable for readers with fundamental knowledge of linear programming, elementary analysis, probability and statistics, and some computer programming background. Several examples of stochastic programming applications are included, providing numerical examples to illustrate the models and algorithms for both stochastic linear and mixed-integer programming, and showing the reader how to implement the models and algorithms using computer software. 410 0$aSpringer Optimization and Its Applications,$x1931-6836 ;$v774 606 $aMathematical optimization 606 $aCalculus of variations 606 $aProbabilities 606 $aComputer science$xMathematics 606 $aNeural networks (Computer science) 606 $aAlgorithms 606 $aDynamical systems 606 $aCalculus of Variations and Optimization 606 $aProbability Theory 606 $aMathematical Applications in Computer Science 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aAlgorithms 606 $aDynamical Systems 615 0$aMathematical optimization. 615 0$aCalculus of variations. 615 0$aProbabilities. 615 0$aComputer science$xMathematics. 615 0$aNeural networks (Computer science). 615 0$aAlgorithms. 615 0$aDynamical systems. 615 14$aCalculus of Variations and Optimization. 615 24$aProbability Theory. 615 24$aMathematical Applications in Computer Science. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aAlgorithms. 615 24$aDynamical Systems. 676 $a519.6 676 $a515.64 700 $aNtaimo$b Lewis$01736374 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910847588003321 996 $aComputational Stochastic Programming$94156217 997 $aUNINA