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

  • Book
  • © 2002

Overview

Authors:
  1. Daniel Bienstock

    1. Department of IEOR, Columbia University, New York

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

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Table of contents (4 chapters)

  1. Front Matter

    Pages i-xix
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  2. Early Algorithms

    Pages 1-25

  3. The Exponential Potential Function - key Ideas

    Pages 27-49

  4. Recent Developments

    Pages 51-72

  5. Computational Experiments Using the Exponential Potential Function Framework

    Pages 73-110

  6. Back Matter

    Pages 103-111
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Keywords

About this book

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.

Authors and Affiliations

  • Department of IEOR, Columbia University, New York

    Daniel Bienstock

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