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Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/CRC Computer & Information Science)

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Synopses & Reviews

Publisher Comments:

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical applications. It is the first book to comprehensively study both approximation algorithms and metaheuristics.

Starting with basic approaches, the handbook presents the methodologies to design and analyze efficient approximation algorithms for a large class of problems, and to establish inapproximability results for another class of problems. It also discusses local search, neural networks, and metaheuristics, as well as multiobjective problems, sensitivity analysis, and stability. After laying this foundation, the book applies the methodologies to classical problems in combinatorial optimization, computational geometry, and graph problems. In addition, it explores large-scale and emerging applications in networks, bioinformatics, VLSI, game theory, and data analysis.

Undoubtedly sparking further developments in the field, this handbook provides the essential techniques to apply approximation algorithms and metaheuristics to a wide range of problems in computer science, operations research, computer engineering, and economics. Armed with this information, researchers can design and analyze efficient algorithms to generate near-optimal solutions for a wide range of computational intractable problems.

Book News Annotation:

For some time this study has received significant attention, and one happy result is the encyclopedic approach taken here in over 85 articles. Covering fundamental and theoretical topics as well as advanced practical applications, this starts with basic approaches and works up to the advanced with full methodologies that include restriction, greedy, relaxation, rounding, primal-dual, local search, transformation and especially metaheuristics. It includes such topics as local search, neural networks, multi-objective optimization, sensitivity analysis, stability, traditional applications, computational geometry and graph applications, and a very interesting range of large-scale and emerging applications. This reference will serve practitioners as well as senior undergraduates and first-year graduate students well, although the enthusiasm and new research it will likely engender may produce a new edition sooner than one might expect. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

The first book to comprehensively study both approximation algorithms and metaheuristics, this resource presents the methodologies to design and analyze efficient approximation algorithms for a large class of problems, and to establish inapproximability results for another class of problems. It also discusses local search, neural networks, and metaheuristics, as well as multiobjective problems, sensitivity analysis, and stability. After laying this foundation, the handbook applies the methodologies to classical problems in combinatorial optimization, computational geometry, and graph problems. In addition, it explores large-scale and emerging applications in networks, bioinformatics, VLSI, game theory, and data analysis.

Product Details

ISBN:
9781584885504
Author:
Gonzalez, Teofilo F.
Publisher:
Chapman & Hall/CRC
Author:
Kovaleva, Sofia
Author:
Gonzalez, Gonzalez F.
Subject:
Programming - General
Subject:
Advanced
Subject:
Mathematical optimization
Subject:
Computer algorithms
Subject:
Mathematics - General
Series:
Chapman & Hall/CRC Computer and Information Science
Series Volume:
10
Publication Date:
20070531
Binding:
Hardcover
Language:
English
Illustrations:
Y
Pages:
1432
Dimensions:
10.05x7.32x2.29 in. 5.27 lbs.

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Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

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Product details 1432 pages Chapman & Hall/CRC - English 9781584885504 Reviews:
"Synopsis" by , The first book to comprehensively study both approximation algorithms and metaheuristics, this resource presents the methodologies to design and analyze efficient approximation algorithms for a large class of problems, and to establish inapproximability results for another class of problems. It also discusses local search, neural networks, and metaheuristics, as well as multiobjective problems, sensitivity analysis, and stability. After laying this foundation, the handbook applies the methodologies to classical problems in combinatorial optimization, computational geometry, and graph problems. In addition, it explores large-scale and emerging applications in networks, bioinformatics, VLSI, game theory, and data analysis.
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