Network Flows: Theory, Algorithms, and Applications, 1st edition

Published by Pearson (February 18, 1993) © 1993

  • Ravindra K. Ahuja Indian Institute of Technology, India
  • Thomas L. Magnanti Massachusetts Institute of Technology
  • James B. Orlin Massachusetts Institute of Technology

  • Hardcover, paperback or looseleaf edition
  • Affordable rental option for select titles
  • Free shipping on looseleafs and traditional textbooks

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.

 1. Introduction.

 2. Paths, Trees and Cycles.

 3. Algorithm Design and Analysis.

 4. Shortest Paths: Label Setting Algorithms.

 5. Shortest Paths: Label Correcting Algorithms.

 6. Maximum Flows: Basic Ideas.

 7. Maximum Flows: Polynomial Algorithms.

 8. Maximum Flows: Additional Topics.

 9. Minimum Cost Flows: Basic Algorithms.

10. Minimum Cost Flows: Polynomial Algorithms.

11. Minimum Cost Flows: Network Simplex Algorithms.

12. Assignments and Matchings.

13. Minimum Spanning Trees.

14. Convex Cost Flows.

15. Generalized Flows.

16. Lagrangian Relaxation and Network Optimization.

17. Multicommodity Flows.

18. Computational Testing of Algorithms.

19. Additional Applications.

Appendix A: Data Structures.

Appendix B: NP-Completeness.

Appendix C: Linear Programming.


Need help? Get in touch

Privacy and cookies
By watching, you agree Pearson can share your viewership data for marketing and analytics for one year, revocable by deleting your cookies.

Pearson eTextbook: What’s on the inside just might surprise you

They say you can’t judge a book by its cover. It’s the same with your students. Meet each one right where they are with an engaging, interactive, personalized learning experience that goes beyond the textbook to fit any schedule, any budget, and any lifestyle.