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E-raamat: Handbook of Approximation Algorithms and Metaheuristics: Methologies and Traditional Applications, Volume 1 2nd edition [Taylor & Francis e-raamat]

Edited by (University of California, Santa Barbara, USA)
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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.



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.