• 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 electronic-only copy of the book. The electronic-only book is published on this website with the permission of Cambridge University Press"
• Hardcover: 518 pages
• eBook: PDF (500 pages)
• Language: English
• ISBN-10: 0521195276
• ISBN-13: 978-0521195270
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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 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.

• 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.

• Amazon

• Algorithms and Data Structures
• Operations Research (OR), Linear Programming, Optimization, and Approximation
• Computational and Algorithmic Mathematics
• Computational Complexity
• Discrete Mathematics

• The Design of Approximation Algorithms (David P. Williamson, et al)
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