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Stochastic Decomposition

A Statistical Method for Large Scale Stochastic Linear Programming

  • Julia L. Higle
  • Suvrajeet Sen

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

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Julia L. Higle, Suvrajeet Sen
    Pages 1-33
  3. Julia L. Higle, Suvrajeet Sen
    Pages 35-61
  4. Julia L. Higle, Suvrajeet Sen
    Pages 63-97
  5. Julia L. Higle, Suvrajeet Sen
    Pages 99-129
  6. Julia L. Higle, Suvrajeet Sen
    Pages 131-164
  7. Julia L. Higle, Suvrajeet Sen
    Pages 165-183
  8. Julia L. Higle, Suvrajeet Sen
    Pages 185-214
  9. Back Matter
    Pages 215-221

About this book

Introduction

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.

Keywords

Mathematica Optimization algorithm Optimization algorithms STATISTICA Simulation algorithms communication linear optimization mathematical programming optimization statistical method stochastic optimization

Authors and affiliations

  • Julia L. Higle
    • 1
  • Suvrajeet Sen
    • 1
  1. 1.University of ArizonaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-4115-8
  • Copyright Information Springer-Verlag US 1996
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6845-8
  • Online ISBN 978-1-4615-4115-8
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site