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Stochastic Decomposition

A Statistical Method for Large Scale Stochastic Linear Programming

  • Book
  • © 1996

Overview

Authors:
  1. Julia L. Higle

    1. University of Arizona, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Suvrajeet Sen

    1. University of Arizona, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Nonconvex Optimization and Its Applications (NOIA, volume 8)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. Two Stage Stochastic Linear Programs

    • Julia L. Higle, Suvrajeet Sen
    Pages 1-33

  3. Sampling Within Stochastic Linear Programming

    • Julia L. Higle, Suvrajeet Sen
    Pages 35-61

  4. Foundations of Stochastic Decomposition

    • Julia L. Higle, Suvrajeet Sen
    Pages 63-97

  5. Stabilizing Stochastic Decomposition

    • Julia L. Higle, Suvrajeet Sen
    Pages 99-129

  6. Stopping Rules for Stochastic Decomposition

    • Julia L. Higle, Suvrajeet Sen
    Pages 131-164

  7. Guidelines for Computer Implementation

    • Julia L. Higle, Suvrajeet Sen
    Pages 165-183

  8. Illustrative Computational Experiments

    • Julia L. Higle, Suvrajeet Sen
    Pages 185-214

  9. Back Matter

    Pages 215-221
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Keywords

About this book

Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air­ line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.

Reviews

`... can be highly recommended to anyone who wants to understand what stochastic decomposition is all about ...'
Mathematical Reviews, 98d

Authors and Affiliations

  • University of Arizona, USA

    Julia L. Higle, Suvrajeet Sen

Bibliographic Information

  • Book Title: Stochastic Decomposition

  • Book Subtitle: A Statistical Method for Large Scale Stochastic Linear Programming

  • Authors: Julia L. Higle, Suvrajeet Sen

  • Series Title: Nonconvex Optimization and Its Applications

  • DOI: https://doi.org/10.1007/978-1-4615-4115-8

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media Dordrecht 1996

  • Hardcover ISBN: 978-0-7923-3840-6Published: 29 February 1996

  • Softcover ISBN: 978-1-4613-6845-8Published: 22 November 2013

  • eBook ISBN: 978-1-4615-4115-8Published: 27 November 2013

  • Series ISSN: 1571-568X

  • Edition Number: 1

  • Number of Pages: XXIV, 222

  • Topics: Optimization, Operations Research/Decision Theory, Systems Theory, Control

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