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Iterative Methods in Combinatorial Optimization

Iterative Methods in Combinatorial Optimization

Iterative Methods in Combinatorial Optimization

Lap Chi Lau, The Chinese University of Hong Kong
R. Ravi, Carnegie Mellon University, Pennsylvania
Mohit Singh, McGill University, Montréal
June 2011
Hardback
9781107007512

    With the advent of approximation algorithms for NP-hard combinatorial optimization problems, several techniques from exact optimization such as the primal-dual method have proven their staying power and versatility. This book describes a simple and powerful method that is iterative in essence and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids and flows. The presentation style is elementary enough to be accessible to anyone with exposure to basic linear algebra and graph theory, making the book suitable for introductory courses in combinatorial optimization at the upper undergraduate and beginning graduate levels. Discussions of advanced applications illustrate their potential for future application in research in approximation algorithms.

    • Presents a unified way of looking at several problems
    • Offers new ways of deriving classical results in optimization
    • Provides extensions to hard variants of classical problems
    • Offers elementary presentation appealing to a broad mathematically interested audience

    Product details

    June 2011
    Hardback
    9781107007512
    254 pages
    235 × 159 × 18 mm
    0.48kg
    44 b/w illus. 102 exercises
    Available

    Table of Contents

    • 1. Introduction
    • 2. Preliminaries
    • 3. Matching and vertex cover in bipartite graphs
    • 4. Spanning trees
    • 5. Matroids
    • 6. Arborescence and rooted connectivity
    • 7. Submodular flows and applications
    • 8. Network matrices
    • 9. Matchings
    • 10. Network design
    • 11. Constrained optimization problems
    • 12. Cut problems
    • 13. Iterative relaxation: early and recent examples
    • 14. Summary.