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Synopsis
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....
Book Description
Designed as a textbook for graduate courses on algorithms, this book will also serve as a reference for researchers who are interested in heuristic solutions of discrete optimization problems. It presents central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization.
About the Author
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...
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- PublisherCambridge University Press
- Publication date2011
- ISBN 10 0521195276
- ISBN 13 9780521195270
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages518
- Rating
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The Design of Approximation Algorithms
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Design of Approximation Algorithms
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Hardcover. Condition: new. Hardcover. 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. Designed as a textbook for graduate courses on algorithms, this book will also serve as a reference for researchers interested in heuristic solutions of discrete optimization problems. It presents central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521195270
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