Advertisement

Potential Function Methods for Approximately Solving Linear Programming Problems

Theory and Practice

  • Daniel┬áBienstock

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 53)

Table of contents

About this book

Introduction

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 Bienstock 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.

Keywords

algorithms design linear optimization mathematical programming optimization programming

Authors and affiliations

  • Daniel┬áBienstock
    • 1
  1. 1.Department of IEORColumbia UniversityNew York

Bibliographic information

  • DOI https://doi.org/10.1007/b115460
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-7173-7
  • Online ISBN 978-0-306-47626-6
  • Series Print ISSN 0884-8289
  • Buy this book on publisher's site