Handbook of Approximation Algorithms and Metaheuristics, Second Edition: Methologies and Traditional Applications, Volume 1

Description

Handbook of Approximation Algorithms and Metaheuristics, Second Editionreflects 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. Content: Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface Contributors 1: Introduction, Overview, and Notation SECTION I: Basic Methodologies 2: Basic Methodologies and Applications 3: Restriction Methods 4: Greedy Methods 5: Recursive Greedy Methods 6: Local Ratio 7: LP Rounding and Extensions 8: Polynomial Time Approximation Schemes 9: Rounding, Interval Partitioning and Separation 10: Asymptotic Polynomial Time Approximation Schemes 11: Randomized Approximation Techniques 12: Distributed Approximation Algorithms via LP-Duality and Randomization 13: Empirical Analysis of Randomised Algorithms14: Reductions That Preserve Approximability 15: Differential Ratio Approximation SECTION II: Local Search, Neural Networks, and Metaheuristics 16: Local Search 17: Stochastic Local Search 18: Very Large-Scale Neighborhood Search: Theory, Algorithms, and Applications 19: Reactive Search: Machine Learning for Memory-Based Heuristics 20: Neural Networks 21: Principles and Strategies of Tabu Search 22: Evolutionary Computation 23: An Introduction to Ant Colony Optimization SECTION III: Multiobjective Optimization, Sensitivity Analysis, and Stability24: Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization: A Review 25: Reoptimization of Hard Optimization Problems 26: Sensitivity Analysis in Combinatorial Optimization 27: Stability of Approximation SECTION IV: Traditional Applications 28: Performance Guarantees for One Dimensional Bin Packing 29: Variants of Classical One Dimensional Bin Packing 30: Variable Sized Bin Packing and Bin Covering 31: Multidimensional Packing Problems 32: Practical Algorithms for Two-Dimensional Packing of Rectangles33: Practical Algorithms for Two-Dimensional Packing of General Shapes 34: Prize Collecting Traveling Salesman and Related Problem 35: A Development and Deployment Framework for Distributed Branch-and-Bound 36: Approximations for Steiner Minimum Trees 37: Practical Approximations of Steiner Trees in Uniform Orientation Metrics 38: Algorithms for Chromatic Sums, Multicoloring, and Scheduling Dependent Jobs 39: Approximation Algorithms and Heuristics for Classical Planning 40: Generalized Assignment Problem 41: Linear Ordering Problem42: Submodular Functions Maximization Problems Index