The Design of Approximation Algorithms

Inbunden, Engelska, 2011

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2011-04-26
Mått:189 x 262 x 34 mm
Vikt:1 120 g
Format:Inbunden
Språk:Engelska
Antal sidor:518
Förlag:Cambridge University Press
ISBN:9780521195270
Utmärkelser:Lanchester Prize 2013

Utforska kategorier

Programmeringsböcker inom Data och IT
Optimering inom Naturvetenskap och teknik
Programvaruutveckling inom Data och IT

Mer om författaren

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. David Shmoys has faculty appointments in both the School of Operations Research and Information Engineering and the Department of Computer Science, and he is currently Associate Director of the Institute for Computational Sustainability at Cornell University. He is a Fellow of the ACM, was an NSF Presidential Young Investigator, and has served on numerous editorial boards, including Mathematics of Operations Research (for which he is currently an associate editor), Operations Research, the ORSA Journal on Computing, Mathematical Programming and both the SIAM Journal on Computing and the SIAM Journal on Discrete Mathematics; he also served as editor-in-chief for the latter.

Recensioner i media

"This is a beautifully written book that will bring anyone who reads it to the current frontiers of research in approximation algorithms. It covers everything from the classics to the latest, most exciting results such as ARV’s sparsest cut algorithm, and does so in an extraordinarily clear, rigorous and intuitive manner." Anna Karlin, University of Washington

Innehållsförteckning

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.