The Design of Approximation Algorithms 1st Edition by David P Williamson ,David B Shmoys ISBN ‎0521195276 978-0521195270

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Product details: 

ISBN 10:0521195276 

ISBN 13:978-0521195270

Author:David P.Williamson ,David B.Shmoys

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.

Table of contents: 

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

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Tags: David P Williamson, David B Shmoys, Approximation , Algorithms