Computational Stochastic Programming by Ntaimo, Lewis;

Models, Algorithms, and Implementation

Series: Springer Optimization and Its Applications; 774;

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Product details:

Edition number 2024
Publisher Springer International Publishing
Date of Publication 5 April 2024
Number of Volumes 1 pieces, Book

ISBN 9783031524622
Binding Hardback
No. of pages509 pages
Size 235x155 mm
Language English
Illustrations XVIII, 509 p. 82 illus., 18 illus. in color. Illustrations, black & white

Categories

Analysis Probability and mathematical statistics Optimization, linear programming, game theory Applied mathematics Taxonomy, systematics Theory of computing, computing in general Computer programming in general Further readings in the field of computing Further readings in mathematics Springer Yellow Sale Analysis (charity campaign) Probability and mathematical statistics (charity campaign) Optimization, linear programming, game theory (charity campaign) Applied mathematics (charity campaign) Taxonomy, systematics (charity campaign) Theory of computing, computing in general (charity campaign) Computer programming in general (charity campaign) Further readings in the field of computing (charity campaign) Further readings in mathematics (charity campaign) Springer Yellow Sale (charity campaign)

Long description:

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 areincluded, 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.

More

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.