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020 _a9783031524646
_9978-3-031-52464-6
024 7 _a10.1007/978-3-031-52464-6
_2doi
050 4 _aQA402.5-402.6
050 4 _aQA315-316
072 7 _aPBU
_2bicssc
072 7 _aMAT042000
_2bisacsh
072 7 _aPBU
_2thema
100 1 _aNtaimo, Lewis.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_974739
245 1 0 _aComputational Stochastic Programming
_h[electronic resource] :
_bModels, Algorithms, and Implementation /
_cby Lewis Ntaimo.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVIII, 509 p. 82 illus., 18 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Optimization and Its Applications,
_x1931-6836 ;
_v774
505 0 _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. .
520 _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.
650 0 _aMathematical optimization.
_965522
650 0 _aCalculus of variations.
_967268
650 0 _aProbabilities.
_965724
650 0 _aComputer science
_xMathematics.
_964041
650 0 _aNeural networks (Computer science) .
_964231
650 0 _aAlgorithms.
_958677
650 0 _aDynamical systems.
650 1 4 _aCalculus of Variations and Optimization.
_967270
650 2 4 _aProbability Theory.
_967428
650 2 4 _aMathematical Applications in Computer Science.
_965518
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_965860
650 2 4 _aAlgorithms.
_958677
650 2 4 _aDynamical Systems.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031524622
776 0 8 _iPrinted edition:
_z9783031524639
830 0 _aSpringer Optimization and Its Applications,
_x1931-6836 ;
_v774
_973211
856 4 0 _uhttps://doi.org/10.1007/978-3-031-52464-6
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c58950
_d58950