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Potential Function Methods for Approximately Solving Linear Programming Problems

Potential Function Methods for Approximately Solving Linear Programming Problems

Potential Function Methods for Approximately Solving Linear Programming Problems
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Potential Function Methods for Approximately Solving Linear Programming Problems Hardback -

by Daniel Bienstock

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Springer , pp. 140 . Hardback. Used.
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Details

  • Title Potential Function Methods for Approximately Solving Linear Programming Problems
  • Author Daniel Bienstock
  • Binding Hardback
  • Edition 1st
  • Condition Used
  • Pages 111
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date pp. 140
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 63079468
  • ISBN 9781402071737 / 1402071736
  • Weight 0.81 lbs (0.37 kg)
  • Dimensions 9.76 x 6.36 x 0.53 in (24.79 x 16.15 x 1.35 cm)
  • Category Mathematics
  • Library of Congress subjects Algorithms, Linear programming
  • Library of Congress Catalogue Number 2002073008
  • Dewey Decimal Code 519.72
  • Quantity available 1

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From the publisher

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.

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