Overview
- Contains detailed numerical examples
- Models real world problems using stochastic programming
- Implements each algorithm using the latest optimization software
Part of the book series: Springer Optimization and Its Applications (SOIA, volume 774)
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About this book
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.
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Keywords
Table of contents (10 chapters)
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Front Matter
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Foundations
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Front Matter
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Modeling and Example Applications
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Front Matter
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Deterministic and Risk-Neutral Decomposition Methods
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Front Matter
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Risk-Averse, Statistical, and Discrete Decomposition Methods
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Front Matter
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Computational Considerations
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Front Matter
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Back Matter
Reviews
“This book is suitable for readers with fundamental knowledge of linear programming, elementary analysis, probability and statistics, and computer programming knowledge needed for model and algorithm implementation using available optimization software. This book is appropriate for students and practitioners who are new to the SP field, and as a reference for the seasoned SP experts.” (I. M. Stancu-Minasian, Mathematical Reviews, April, 2025)
Authors and Affiliations
Bibliographic Information
Book Title: Computational Stochastic Programming
Book Subtitle: Models, Algorithms, and Implementation
Authors: Lewis Ntaimo
Series Title: Springer Optimization and Its Applications
DOI: https://doi.org/10.1007/978-3-031-52464-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-52462-2Published: 05 April 2024
eBook ISBN: 978-3-031-52464-6Published: 04 April 2024
Series ISSN: 1931-6828
Series E-ISSN: 1931-6836
Edition Number: 1
Number of Pages: XVIII, 509
Number of Illustrations: 64 b/w illustrations, 18 illustrations in colour
Topics: Optimization, Probability Theory and Stochastic Processes, Mathematical Applications in Computer Science, Mathematical Models of Cognitive Processes and Neural Networks, Algorithms, Dynamical Systems and Ergodic Theory