Network Flows: Theory, Algorithms, and Applications

Ahuja, Ravindra, Magnanti, Thomas, Orlin, James

ISBN 10: 013617549X / ISBN 13: 9780136175490

Published by Pearson, 1993

About this Item

Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Seller Inventory # 000198207U

Bibliographic Details

• Title: Network Flows: Theory, Algorithms, and Applications
• Publisher: Pearson
• Publication Date: 1993
• Language: English
• ISBN 10: 013617549X
• ISBN 13: 9780136175490
• Binding: hardcover
• Edition: 1st Edition.
• Condition: Good

About this title

Synopsis

A comprehensive introduction to network flows that brings together the classic and the contemporary aspects of the field, and provides an integrative view of theory, algorithms, and applications.

presents in-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models.

• emphasizes powerful algorithmic strategies and analysis tools such as data scaling, geometric improvement arguments, and potential function arguments.

• provides an easy-to-understand descriptions of several important data structures, including d-heaps, Fibonacci heaps, and dynamic trees.

• devotes a special chapter to conducting empirical testing of algorithms.

• features over 150 applications of network flows to a variety of engineering, management, and scientific domains.

• contains extensive reference notes and illustrations.    

From the Back Cover

Bringing together the classic and the contemporary aspects of the field, this comprehensive introduction to network flows provides an integrative view of theory, algorithms, and applications.

It offers in-depth and self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including a description of new and novel polynomial-time algorithms for these core models. For professionals working with network flows, optimization, and network programming.