The design of approximation algorithms / David P. Williamson, David B. Shmoys.
"Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P =...
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| Language: | English |
| Published: |
New York :
Cambridge University Press,
2011.
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| Subjects: | |
| Physical Description: | xi, 504 pages : illustrations ; 26 cm |
| Format: | Book |
MARC
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| 003 | OCoLC | ||
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| 020 | |a 9780521195270 (hardback) | ||
| 020 | |a 0521195276 (hardback) | ||
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| 100 | 1 | |a Williamson, David P. |0 http://id.loc.gov/authorities/names/nb2007016384 | |
| 245 | 1 | 4 | |a The design of approximation algorithms / |c David P. Williamson, David B. Shmoys. |
| 260 | |a New York : |b Cambridge University Press, |c 2011. | ||
| 300 | |a xi, 504 pages : |b illustrations ; |c 26 cm | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and indexes. | ||
| 505 | 8 | |a Machine generated contents note: 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. | |
| 520 | |a "Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. 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 part of the book is devoted to a single algorithmic technique, which is then applied to several different problems. The second part revisits the techniques but offers more sophisticated treatments of them. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems"-- |c Provided by publisher. | ||
| 650 | 0 | |a Approximation theory. |0 http://id.loc.gov/authorities/subjects/sh85006190 | |
| 650 | 0 | |a Mathematical optimization. |0 http://id.loc.gov/authorities/subjects/sh85082127 | |
| 650 | 7 | |a COMPUTERS / Programming / Algorithms. |2 bisacsh | |
| 700 | 1 | |a Shmoys, David Bernard. |0 http://id.loc.gov/authorities/names/n86840752 | |
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