[share_ebook] Download The Design of Approximation Algorithms Free PDF eBook

ISBN: 0521195276

Language: English

Format: pdf

Category: Technical

Tag: Programming


Posted on 2011-03-25, by samsexy98.

Description

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Author(s): David P. Williamson, David B. Shmoys
Publisher: Cambridge University Press
Date : 2011
Pages : 496
Format : PDF
Language : English
ISBN-10 : 0521195276
ISBN-13 : 978-0521195270
Size : 2.16 MB

 


Description:

This eBook is designed as a textbook for graduate-level courses on algorithms. This eBook will also serve as a reference for researchers who are interested in heuristic solutions of discrete optimization problems. It presents central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization.

 

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 eBook shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The eBook 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 eBook 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 eBook also covers methods for proving that optimization problems are hard to approximate.

 

Reviews

 

This is a beautifully written book that will bring anyone who reads it to the current frontiers of research in approximation algorithms. It covers everything from the classics to the latest, most exciting results such as ARV's sparsest cut algorithm, and does so in an extraordinarily clear, rigorous and intuitive manner.

--Anna Karlin, University of Washington

 

This book on approximation algorithms is a beautiful example of an ideal textbook. It gives a concise treatment of the major techniques, results and references in approximation algorithms and provides an extensive and systematic coverage of this topic up to the frontier of current research. It will become a standard textbook and reference for graduate students, teachers and researchers in the field.

--Rolf H. Möhring, Technische Universität Berlin

 

I have fond memories of learning approximation algorithms from an embryonic version of this book. The reader can expect a clearly written and thorough tour of all the important paradigms for designing efficient heuristics with provable performance guarantees for combinatorial optimization problems.

--Tim Roughgarden, Stanford University

 

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