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Published **July 26, 1996**
by Course Technology .

Written in English

- Mathematical theory of computation,
- Programming (Mathematics),
- Linear Programming,
- Mathematics,
- Computers - Languages / Programming,
- Science/Mathematics,
- Programming Languages - General,
- Discrete Mathematics,
- Computers / Computer Science,
- Algorithms,
- Approximation Theory

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 624 |

ID Numbers | |

Open Library | OL7786857M |

ISBN 10 | 0534949681 |

ISBN 10 | 9780534949686 |

The first part of the book presents a set of classical NP hard problems, set covering, bin packing, knapsack, etc. and their approximation algorithms. These algorithms are extracted from a number of fundamental papers, which are of long, delicate presentations. Vazirami presented the problems and solutions in a unified by: Approximation Algorithms for NP-Hard Problems Edited by Dorit S. Hochbaum Published July The thirteen chapters of the book are written by leading researchers that have contributed to the state of the art of approximation algorithms. About the book. Table of Contents. ISBN: OCLC Number: Description: xxii, pages: illustrations ; 24 cm: Contents: Approximation algorithm for scheduling / Leslie A. Hall --Approximation algorithms for bin packing: a survey / E.G. Coffmann, Jr., M.R. Garey, and D.S. Johnson --Approximating covering and packing problems: set cover, vertex cover, . APPROXIMATION ALGORITHMS FOR NP-HARD PROBLEMS is intended for computer scientists and operations With chapters contributed by leading researchers in the field, this book introduces unifying techniques in the analysis of approximation algorithms/5(12).

Approximation algorithms have developed in response to the impossibility of solving a great variety of important optimization problems. Too frequently, when attempting to get a solution for a problem, one is confronted with the fact that the problem is : S HochbaDorit. From the introduction: This book deals with designing polynomial time approximation algorithms for NP-hard optimization problems. Typically, the decision versions of these problems are in NP, and are therefore NP-complete. From the viewpoint of exact solutions, all NP-complete problems are equally hard, since they are inter-reducible via polynomial time reductions. With chapters contributed by leading researchers in the field, this book introduces unifying techniques in the analysis of approximation algorithms. APPROXIMATION ALGORITHMS FOR NP-HARD PROBLEMS is intended for computer scientists and operations researchers interested in specific algorithm implementations, as well as design tools for algorithms. Although this may seem a paradox, all exact science is dominated by the idea of approximation. Bertrand Russell () Most natural optimization problems, including those arising in important application areas, are NP-hard. Therefore, under the widely believed con jecture that P -=/= NP, their exact solution is prohibitively time consuming/5.

Although this may seem a paradox, all exact science is dominated by the idea of approximation. Bertrand Russell () Most natural optimization problems, including those arising in important application areas, are NP-hard. Therefore, under the widely believed con jecture that P -=/= NP, their exact solution is prohibitively time consuming.4/5(4). Approximation Algorithms for NP-Hard P roblems algorithms that are eﬃcient in rela tively small inputs, may become impractica l for input sizes of sev eral gigabytes. approximate solutions to NP-hard discrete optimization problems. At one or two points in the book, we do an NP-completeness reduction to show that it can be hard to ﬁnd approximate solutions to such problems; we include a short appendix on the problem class NP and the notion of NP-completeness for those unfamiliar with the concepts. Yet most interesting discrete optimization 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.

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