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Handbook of Approximation Algorithms and Metaheuristics, Second Edition

Methodologies and Traditional Applications

Edited by: Teofilo F. Gonzalez

Print publication date:  May  2018
Online publication date:  May  2018

Print ISBN: 9781498770118
eBook ISBN: 9781351236423
Adobe ISBN:

10.1201/9781351236423
 Cite  Marc Record

Book description

Handbook of Approximation Algorithms and Metaheuristics, Second Edition reflects the tremendous growth in the field, over the past two decades. Through contributions from leading experts, this handbook provides a comprehensive introduction to the underlying theory and methodologies, as well as the various applications of approximation algorithms and metaheuristics.

Volume 1 of this two-volume set deals primarily with methodologies and traditional applications. It includes restriction, relaxation, local ratio, approximation schemes, randomization, tabu search, evolutionary computation, local search, neural networks, and other metaheuristics. It also explores multi-objective optimization, reoptimization, sensitivity analysis, and stability. Traditional applications covered include: bin packing, multi-dimensional packing, Steiner trees, traveling salesperson, scheduling, and related problems.

Volume 2 focuses on the contemporary and emerging applications of methodologies to problems in combinatorial optimization, computational geometry and graphs problems, as well as in large-scale and emerging application areas. It includes approximation algorithms and heuristics for clustering, networks (sensor and wireless), communication, bioinformatics search, streams, virtual communities, and more.

About the Editor

Teofilo F. Gonzalez is a professor emeritus of computer science at the University of California, Santa Barbara. He completed his Ph.D. in 1975 from the University of Minnesota. He taught at the University of Oklahoma, the Pennsylvania State University, and the University of Texas at Dallas, before joining the UCSB computer science faculty in 1984. He spent sabbatical leaves at the Monterrey Institute of Technology and Higher Education and Utrecht University. He is known for his highly cited pioneering research in the hardness of approximation; for his sublinear and best possible approximation algorithm for k-tMM clustering; for introducing the open-shop scheduling problem as well as algorithms for its solution that have found applications in numerous research areas; as well as for his research on problems in the areas of job scheduling, graph algorithms, computational geometry, message communication, wire routing, etc.

Table of contents

Prelims Download PDF
Chapter  1:  Introduction, Overview, and Notation Download PDF
Chapter  2:  Basic Methodologies and Applications Download PDF
Chapter  3:  Restriction Methods Download PDF
Chapter  4:  Greedy Methods Download PDF
Chapter  5:  Recursive Greedy Methods Download PDF
Chapter  6:  Local Ratio Download PDF
Chapter  7:  LP Rounding and Extensions Download PDF
Chapter  8:  Polynomial Time Approximation Schemes Download PDF
Chapter  9:  Rounding, Interval Partitioning and Separation Download PDF
Chapter  10:  Asymptotic Polynomial Time Approximation Schemes Download PDF
Chapter  11:  Randomized Approximation Techniques Download PDF
Chapter  12:  Distributed Approximation Algorithms via LP-Duality and Randomization Download PDF
Chapter  13:  Empirical Analysis of Randomised Algorithms Download PDF
Chapter  14:  Reductions That Preserve Approximability Download PDF
Chapter  15:  Differential Ratio Approximation Download PDF
Chapter  16:  Local Search Download PDF
Chapter  17:  Stochastic Local Search Download PDF
Chapter  18:  Very Large-Scale Neighborhood Search: Theory, Algrithms, and Applications Download PDF
Chapter  19:  Reactive Search: Machine Learning for Memory-Based Heuristics Download PDF
Chapter  20:  Neural Networks Download PDF
Chapter  21:  Principles and Strategies of Tabu Search Download PDF
Chapter  22:  Evolutionary Computation Download PDF
Chapter  23:  An Introduction to Ant Colony Optimization Download PDF
Chapter  24:  Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization: A Review Download PDF
Chapter  25:  Reoptimization of Hard Optimization Problems Download PDF
Chapter  26:  Sensitivity Analysis in Combinatorial Optimization  Download PDF
Chapter  27:  Stability of Approximation Download PDF
Chapter  28:  Performance Guarantees for One Dimensional Bin Packing  Download PDF
Chapter  29:  Variants of Classical One Dimensional Bin Packing  Download PDF
Chapter  30:  Variable Sized Bin Packing and Bin Covering  Download PDF
Chapter  31:  Multidimensional Packing Problems Download PDF
Chapter  32:  Practical Algorithms for Two-Dimensional Packing of Rectangles Download PDF
Chapter  33:  Practical Algorithms for Two-Dimensional Packing of General Shapes Download PDF
Chapter  34:  Prize Collecting Traveling Salesman and Related Problems Download PDF
Chapter  35:  A Development and Deployment Framework for Distributed Branch-and-Bound Download PDF
Chapter  36:  Approximations for Steiner Minimum Trees Download PDF
Chapter  37:  Practical Approximations of Steiner Trees in Uniform Orientation Metrics Download PDF
Chapter  38:  Algorithms for Chromatic Sums, Multicoloring, and Scheduling Dependent Jobs Download PDF
Chapter  39:  Approximation Algorithms and Heuristics for Classical Planning Download PDF
Chapter  40:  Generalized Assignment Problem Download PDF
Chapter  41:  Linear Ordering Problem Download PDF
Chapter  42:  Submodular Functions Maximization Problems Download PDF
Index Download PDF
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