Handbook of Approximation Algorithms and Metaheuristics
Methologies and Traditional Applications, Volume 1
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
Part 1: Basic Methodologies 1. Introduction, Overview and Definitions 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 Algorithms 14. Reductions that Preserve Approximability 15. Differential Ratio Approximation Part 2: Local Search, Neural Networks, and Meta-heuristics 16. Local Search 17. Stochastic Local Search 18. Very Large Neighborhood Search 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 Part 3: Multiobjective Optimization, Sensitivity Analysis and Stability 24. 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 Part 4: 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 Rectangles 33. Practical Algorithms for Two-dimensional Packing of General Shapes 34. Prize Collecting Traveling Salesman and Related Problems 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 39. Approximation Algorithms and Heuristics for Classical Planning 40. Generalized Assignment Problem 41. Probabilistic Greedy Heuristics for Satisfiability Problems 42. Linear Ordering Problem 43. Submodular FunctionsMaximization Problems
Teofilo Gonzalez is a professor of computer science at the University of California, Santa Barbara.