David P. Williamson

Design of Approximation Algorithms (eBook, PDF)

• Format: PDF

Produktbeschreibung

Discrete optimization problems are everywhere, from traditional operations research planning (scheduling, facility location and network design); to computer science databases; to advertising issues in viral marketing. Yet most such problems are NP-hard; unless P = NP, there are no efficient algorithms to find optimal solutions. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first section is devoted to a single algorithmic technique applied to several different problems, with more sophisticated treatment in the second section. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithm courses, it will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 26. April 2011
• Englisch
• ISBN-13: 9781139065153
• Artikelnr.: 56681281

Autorenporträt

David P. Williamson is a Professor at Cornell University with a joint appointment in the School of Operations Research and Information Engineering and in the Department of Information Science. Prior to joining Cornell, he was a Research Staff Member at the IBM T. J. Watson Research Center and a Senior Manager at the IBM Almaden Research Center. He has won several awards for his work on approximation algorithms, including the 2000 Fulkerson Prize, sponsored by the American Mathematical Society and the Mathematical Programming Society. He has served on several editorial boards, including ACM Transactions on Algorithms, Mathematics of Operations Research, the SIAM Journal on Computing and the SIAM Journal on Discrete Mathematics.

Inhaltsangabe

Part I. An Introduction to the Techniques: 1. An introduction to approximation algorithms
2. Greedy algorithms and local search
3. Rounding data and dynamic programming
4. Deterministic rounding of linear programs
5. Random sampling and randomized rounding of linear programs
6. Randomized rounding of semidefinite programs
7. The primal-dual method
8. Cuts and metrics
Part II. Further Uses of the Techniques: 9. Further uses of greedy and local search algorithms
10. Further uses of rounding data and dynamic programming
11. Further uses of deterministic rounding of linear programs
12. Further uses of random sampling and randomized rounding of linear programs
13. Further uses of randomized rounding of semidefinite programs
14. Further uses of the primal-dual method
15. Further uses of cuts and metrics
16. Techniques in proving the hardness of approximation
17. Open problems
Appendix A. Linear programming
Appendix B. NP-completeness.