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

Daniel Bienstock

ISBN 10: 1402071736 / ISBN 13: 9781402071737

Published by Springer US, Springer New York Aug 2002, 2002

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.

Seller Inventory # 9781402071737

Bibliographic Details

• Title: Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice
• Publisher: Springer US, Springer New York Aug 2002
• Publication Date: 2002
• Language: English
• ISBN 10: 1402071736
• ISBN 13: 9781402071737
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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.