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 Title The Design of Approximation Algorithms
 Author(s) David P. Williamson and David B. Shmoys
 Publisher: Cambridge University Press, 1 edition (2011); eBook (Manuscript)
 Permission: On the Download Page: "Below you can download an electroniconly copy of the book. The electroniconly book is published on this website with the permission of Cambridge University Press"
 Hardcover 518 pages
 eBook PDF (500 pages)
 Language: English
 ISBN10: 0521195276
 ISBN13: 9780521195270
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Book Description
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 NPhard.
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 nearoptimal 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 graduatelevel algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.
About the Authors 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.
 David Shmoys has faculty appointments in both the School of Operations Research and Information Engineering and the Department of Computer Science at Cornell University.
 Algorithms and Data Structures
 Operations Research (OR), Linear Programming, Optimization, and Approximation
 Computational and Algorithmic Mathematics
 Computational Complexity
 Discrete Mathematics
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