Designed as a textbook for graduate courses on algorithms, this book presents efficient algorithms that find provably near-optimal solutions.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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