Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice

by Daniel Bienstock

Overview

Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground in linear programming theory. The book draws on the research developments in three broad areas: linear and integer programming, numerical analysis, and the computational architectures which enable speedy, high-level algorithm design. During the last ten years, a new body of research within the field of optimization research has emerged, which seeks to develop good approximation algorithms for classes of linear programming problems. This work both has roots in fundamental areas of mathematical programming and is also framed in the context of the modern theory of algorithms. The result of this work, in which Daniel Biensk has been very much involved, has been a family of algorithms with solid theoretical foundations and with growing experimental success. This book will examine these algorithms, starting with some of the very earliest examples, and through the latest theoretical and computational developments.

Product Details

ISBN-13:	9781475776720
Publisher:	Springer US
Publication date:	03/17/2013
Series:	International Series in Operations Research & Management Science , #53
Edition description:	Softcover reprint of the original 1st ed. 2002
Pages:	111
Product dimensions:	6.10(w) x 9.25(h) x 0.01(d)

Table of Contents

Early Algorithms.- The Exponential Potential Function - key Ideas.- Recent Developments.- Computational Experiments Using the Exponential Potential Function Framework.