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Computational stochastic programming = models, algorithms, and implementation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational stochastic programming/ by Lewis Ntaimo.
其他題名:	models, algorithms, and implementation /
作者:	Ntaimo, Lewis.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xviii, 509 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Stochastic programming. -
電子資源:	https://doi.org/10.1007/978-3-031-52464-6
ISBN:	9783031524646

Computational stochastic programming = models, algorithms, and implementation /
Ntaimo, Lewis.

Computational stochastic programmingmodels, algorithms, and implementation /[electronic resource] :by Lewis Ntaimo. - Cham :Springer International Publishing :2024. - xviii, 509 p. :ill. (some col.), digital ;24 cm. - Springer optimization and its applications,v. 7741931-6836 ;. - Springer optimization and its applications ;v. 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.

ISBN: 9783031524646

Standard No.: 10.1007/978-3-031-52464-6doiSubjects--Topical Terms:

647620
Stochastic programming.

LC Class. No.: T57.79

Dewey Class. No.: 519.7

Computational stochastic programming = models, algorithms, and implementation /
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505 0 $a 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.
520 $a 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.
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