Handbook of Approximation Algorithms and Metaheuristics Contemporary and Emerging Applications Volume 2 2nd Edition by Teofilo F Gonzalez ISBN 0367571595 978-0367571597

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Product details:

ISBN 10: 0367571595 

ISBN 13: 978-0367571597 

Author:  Teofilo F Gonzalez

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 I: Computational Geometry and Graph Applications

1. Introduction, Overview, and Notation

2. Approximation Schemes for Minimum-Cost k-Connectivity Problems in Geometric Graphs

3. Dilation and Detours in Geometric Networks

4. The Well-Separated Pair Decomposition and Its Applications

5. Covering with Unit Balls

6. Minimum Edge-Length Rectangular Partitions

7. Automatic Placement of Labels in Maps and Drawings

8. Complexity, Approximation Algorithms, and Heuristics for the Corridor Problems

9. Approximate Clustering

10. Maximum Planar Subgraph

11. Disjoint Paths and Unsplittable Flow

12. The k-Connected Subgraph Problem

13. Node-Connectivity Survivable Network Problems

14. Optimum Communication Spanning Trees

15. Activation Network Design Problems

16. Stochastic Local Search Algorithms for the Graph Coloring Problem

17. On Solving the Maximum Disjoint Paths Problem with Ant Colony Optimization

18. Efficient Approximation Algorithms in Random Intersection Graphs

19. Approximation Algorithms for Facility Dispersion

Part II: Large-Scale and Emerging Applications

20. Cost-Efficient Multicast Routing in Ad Hoc and Sensor Networks

21. Approximation Algorithm for Clustering in Mobile Ad-Hoc Networks

22. Topology Control Problems for Wireless Ad Hoc Networks

23. QoS Multimedia Multicast Routing

24. Overlay Networks for Peer-to-Peer Networks

25. Data Broadcasts on Multiple Wireless Channels: Exact and Time-Optimal Solutions for Uniform Data and Heuristics for Nonuniform Data

26. Strategies for Aggregating Time-Discounted Information in Sensor Networks

27. Approximation and Exact Algorithms for Optimally Placing a Limited Number of Storage Nodes in a Wireless Sensor Network

28. Approximation Algorithms for the Primer Selection, Planted Motif Search, and Related Problems

29. Dynamic and Fractional Programming-Based Approximation Algorithms for Sequence Alignment with Constraints

30. Approximation Algorithms for the Selection of Robust Tag SNPs

31. Large-Scale Global Placement

32. Histograms, Wavelets, Streams, and Approximation

33. Color Quantization

34. A GSO-Based Swarm Algorithm for Odor Source Localization in Turbulent Environments

35. Digital Reputation for Virtual Communities

36. Approximation for Influence Maximization

37. Approximation and Heuristics for Community Detection

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Tags: Teofilo F Gonzalez, Handbook, Approximation Algorithms, Metaheuristics, Contemporary, Emerging Applications