04117nam a22006375i 4500 978-3-031-52464-6 DE-He213 20251030111805.0 cr nn 008mamaa 240404s2024 sz | s |||| 0|eng d 9783031524646 978-3-031-52464-6 10.1007/978-3-031-52464-6 doi QA402.5-402.6 QA315-316 PBU bicssc MAT042000 bisacsh PBU thema Ntaimo, Lewis. author. aut http://id.loc.gov/vocabulary/relators/aut 74739 Computational Stochastic Programming [electronic resource] : Models, Algorithms, and Implementation / by Lewis Ntaimo. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XVIII, 509 p. 82 illus., 18 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Optimization and Its Applications, 1931-6836 ; 774 1. 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. . This 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. Mathematical optimization. 65522 Calculus of variations. 67268 Probabilities. 65724 Computer science Mathematics. 64041 Neural networks (Computer science) . 64231 Algorithms. 58677 Dynamical systems. Calculus of Variations and Optimization. 67270 Probability Theory. 67428 Mathematical Applications in Computer Science. 65518 Mathematical Models of Cognitive Processes and Neural Networks. 65860 Algorithms. 58677 Dynamical Systems. SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031524622 Printed edition: 9783031524639 Springer Optimization and Its Applications, 1931-6836 ; 774 73211 https://doi.org/10.1007/978-3-031-52464-6 ZDB-2-SMA ZDB-2-SXMS 58950 58950