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The Design of Approximation Algorithms, by David P. Williamson, David B. Shmoys
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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.
- Sales Rank: #370519 in Books
- Published on: 2011-04-26
- Original language: English
- Number of items: 1
- Dimensions: 9.96" h x 1.14" w x 8.46" l, 2.35 pounds
- Binding: Hardcover
- 518 pages
Review
"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
"The authors of this book are leading experts in the area of approximation algorithms. They do a wonderful job in providing clear and unified explanations of subjects ranging from basic and fundamental algorithmic design techniques to advanced results in the forefront of current research. This book will be very valuable to students and researchers alike."
Uriel Feige, Professor of Computer Science and Applied Mathematics, the Weizmann Institute
"Theory of approximation algorithms is one of the most exciting areas in theoretical computer science and operations research. This book, written by two leading researchers, systematically covers all the important ideas needed to design effective approximation algorithms. The description is lucid, extensive and up-to-date. This will become a standard textbook in this area for graduate students and researchers."
Toshihide Ibaraki, The Kyoto College of Graduate Studies for Informatics
"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
"This book is very well written. It could serve as a textbook on the design of approximation algorithms for discrete optimization problems. Readers will enjoy the clear and precise explanation of modern concepts, and the results obtained in this very elegant theory. Solving the exercises will benefit all readers interested in gaining a deeper understanding of the methods and results in the approximate algorithms for discrete optimization area."
Alexander Kreinin, Computing Reviews
"Any researcher interested in approximation algorithms would benefit greatly from this new book by Williamson and Schmoys. It is an ideal starting point for the fresh graduate student, as well as an excellent reference for the experts in the field. The wrting style is very clear and lucid, and it was a pleasure reading and reviewing this book."
Deeparnab Chakrabarty for SIGACT News
"The structure of the book is very interesting and allows a deeper understanding of the techniques presented. The whole book manages to develop a way of analyzing approximation algorithms and of designing approximation algorithms that perform well."
Dana Simian, Mathematical Reviews
About the Author
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. Prior to joining Cornell, he was a Research Staff Member at the IBM T. J. Watson Research Center and a Senior Manager at the IBM Almaden Research Center. He has won several awards for his work on approximation algorithms, including the 2000 Fulkerson Prize, sponsored by the American Mathematical Society and the Mathematical Programming Society. He has served on several editorial boards, including ACM Transactions on Algorithms, Mathematics of Operations Research, the SIAM Journal on Computing and the SIAM Journal on Discrete Mathematics.
David Shmoys has faculty appointments in both the School of Operations Research and Information Engineering and the Department of Computer Science, and he is currently Associate Director of the Institute for Computational Sustainability at Cornell University. He is a Fellow of the ACM, was an NSF Presidential Young Investigator, and has served on numerous editorial boards, including Mathematics of Operations Research (for which he is currently an associate editor), Operations Research, the ORSA Journal on Computing, Mathematical Programming and both the SIAM Journal on Computing and the SIAM Journal on Discrete Mathematics; he also served as editor-in-chief for the latter.
Most helpful customer reviews
5 of 5 people found the following review helpful.
A well presented mathematical reference book on the different ways approximation algorithms can be built
By Litsios James
Hidden in back of the dual gap that arises in the approximate formulation of primal and dual solutions to an optimization problem is a hidden world of magic. This book is a great reference on discovering this magic, as it presents the multiple ways these approximations algorithms can be built, adapted to different problem domains.
The book is not an easy read, and therefore my usage of the term "reference". Each chapter takes a class of problems, presents a class of approximation algorithms adapted to it, and follows with examples of applying these algorithms. The later chapters build upon the learning of the earlier chapters. The emphasis is on the mathematical properties of the methods presented. And I will admit that while I did manage to enjoy the first third of the book, I was a little put off by its "cold" mathematical formulation. I am sure that a pure mathematician would have not issue with this, but my "applied" side would have wanted many more examples, and a bit more "prose". And therefore my four stars "only" review.
1 of 2 people found the following review helpful.
Great book and looking forward the next edition
By Deng
This is a great book, well organized, and covers lots of material. We use it as our course material and it really helps a lot.
0 of 1 people found the following review helpful.
Excellent book with detailed introduction to modern concepts of discrete ...
By Jun Qi
Excellent book with detailed introduction to modern concepts of discrete optimization. The paper of the book seems to be pretty good as well.
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