Handbook of Approximation Algorithms and Metaheuristics : Methologies and Traditional Applications, Volume 1 book cover
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Handbook of Approximation Algorithms and Metaheuristics
Methologies and Traditional Applications, Volume 1





ISBN 9780367571580
Published June 30, 2020 by Chapman and Hall/CRC
816 Pages

 
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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

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

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Editor(s)

Biography

Teofilo Gonzalez is a professor of computer science at the University of California, Santa Barbara.