The Design of Approximation Algorithms /
Williamson, David P.,
The Design of Approximation Algorithms / David P. Williamson, David B. Shmoys. - 1 online resource (518 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511921735 (ebook)
Approximation theory
Mathematical optimization
QA221 / .W55 2011
518/.5
The Design of Approximation Algorithms / David P. Williamson, David B. Shmoys. - 1 online resource (518 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511921735 (ebook)
Approximation theory
Mathematical optimization
QA221 / .W55 2011
518/.5