Handbook of Approximation Algorithms and Metaheuristics

Teofilo F. Gonzalez

May 15, 2007 by Chapman and Hall/CRC
Reference - 1432 Pages - 271 B/W Illustrations
ISBN 9781584885504 - CAT# C5505
Series: Chapman & Hall/CRC Computer and Information Science Series


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  • Describes basic methodologies that include restriction, greedy, relaxation, rounding, primal-dual, local search, transformation, and metaheuristics
  • Explains polynomial time and fully polynomial time approximation schemes, including standard, asymptotic, and randomized
  • Reviews approximation algorithms for bin packing, the traveling sales person problem, and Steiner trees
  • Discusses computational geometry and graph applications, such as triangulations, pair decompositions, partitioning, maximum planar subgraphs, edge disjoint paths and unsplittable flow, and communication spanning trees
  • Presents the latest algorithmic applications, including wireless ad hoc networks, microarray analysis, and global routing
  • Summary

    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.