Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming / Edition 1

by Julia L. Higle, S. Sen

Overview

Motivation Shastic 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.

Product Details

ISBN-13:	9780792338406
Publisher:	Springer US
Publication date:	02/29/1996
Series:	Nonconvex Optimization and Its Applications , #8
Edition description:	1996
Pages:	222
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Preface. 1. Two Stage Shastic Linear Programs. 2. Sampling Within Shastic Linear Programming. 3. Foundations of Shastic Decomposition. 4. Stabilizing Shastic Decomposition. 5. Stopping Rules for Shastic Decomposition. 6. Guidelines for Computer Implementation. 7. Illustrative Computational Experiments. Glossary.

Editorial Reviews

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

From the Publisher

Summarizes developments related to a class of methods called stochastic decomposition algorithms, which depart from traditional deterministic algorithms by combining sampling approaches from the statistical literature with traditional mathematical programming constructs such as decomposition or cutting planes. The combination results in a method that is driven by computational considerations and is extremely flexible in accommodating various representations of uncertainty. Of interest to researchers in mathematical optimization. No index. Double spaced. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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