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Iterative methods in combinatorial optimization [electronic resource] / Lap Chi Lau, R. Ravi, Mohit Singh.
Material type:
TextSeries: Cambridge texts in applied mathematicsPublication details: Cambridge ; New York : Cambridge University Press, 2011.Description: xi, 242 p. : illSubject(s): Genre/Form: DDC classification: - 518/.26 22
- QA297.8 .L38 2011
Contents:
Summary: "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"-- Provided by publisher.Summary: "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"-- Provided by publisher.
Machine generated contents note: 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.
