BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice (International Series in Operations Research & Management Science, 53)

Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice (International Series in Operations Research & Management Science, 53)

Potential Function Methods for Approximately Solving Linear Programming
Stock photo: cover may vary

Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice (International Series in Operations Research & Management Science, 53) Hardback - 2002

by Bienstock, Daniel

Add to wish list
  • Used
  • Good
  • Hardback
Used - Good

Description

hardcover. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
Ask the seller a question Add to wish list
A$164.48
Free Delivery within USA
Standard delivery: 7 to 14 days
More delivery options
Dropship order
Ships from Bonita (California, United States)

Details

About Bonita California, United States

Biblio member since 2020

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Bonita

Reader reviews for Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice (International Series in Operations Research & Management Science, 53)

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.
tracking-