Lewis Ntaimo

Computational Stochastic Programming

Models, Algorithms, and Implementation

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Springer Optimization and Its Applications 774
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-52462-2
• 2024
• Seitenzahl: 528
• Erscheinungstermin: 5. April 2024
• Englisch
• Abmessung: 241mm x 160mm x 34mm
• Gewicht: 895g
• ISBN-13: 9783031524622
• ISBN-10: 3031524624
• Artikelnr.: 69634557

Inhaltsangabe

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.