Designed as a textbook for graduate courses on algorithms, this book presents efficient algorithms that find provably near-optimal solutions.
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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.
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Zustand: New. 2011. 1st Edition. Hardcover. Designed as a textbook for graduate courses on algorithms, this book presents efficient algorithms that find provably near-optimal solutions. Num Pages: 518 pages, 86 b/w illus. 121 exercises. BIC Classification: PBU; UMB; UMZ. Category: (U) Tertiary Education (US: College). Dimension: 256 x 186 x 32. Weight in Grams: 1154. 516 pages, 86 b/w illus. 121 exercises. Designed as a textbook for graduate courses on algorithms, this book presents efficient algorithms that find provably near-optimal solutions. Cateogry: (U) Tertiary Education (US: College). BIC Classification: PBU; UMB; UMZ. Dimension: 256 x 186 x 32. Weight: 1086. . . . . . Books ship from the US and Ireland. Artikel-Nr. V9780521195270
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9780521195270
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