The Design of Approximation Algorithms / by D. P. Williamson and D. B. Shmoys. [Electronic Resource]
Material type: Computer filePublication details: Cambridge : Cambridge University Press, 2011Description: xii, 504pISBN:- 9780511921735
- 518.5Â W676D
Item type | Home library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | Textbook | 518.5 W676D (Browse shelf(Opens below)) | Available (e-Book For Access) | Platform : Cambridge Core | EB0408 |
Browsing S. R. Ranganathan Learning Hub shelves, Shelving location: Online, Collection: Textbook Close shelf browser (Hides shelf browser)
518.02462 G959N Numerical Methods for Engineers | 518.1 Ab51A The Age of Algorithms | 518.1 Ed57H How to Think About Algorithms | 518.5 W676D The Design of Approximation Algorithms | 518.64 L269A Advanced Topics in Computational Partial Differential Equations : Numerical Methods and Diffpack Programming | 518.64 M846N Numerical Solution of Partial Differential Equations : An Introduction | 519.2 B849B Basic Stochastic Processes : A Course Through Exercises |
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.
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