Muutke küpsiste eelistusi

Handbook of Approximation Algorithms and Metaheuristics: Contemporary and Emerging Applications, Volume 2 2nd edition [Pehme köide]

Edited by (University of California, Santa Barbara, USA)
Teised raamatud teemal:
Teised raamatud teemal:

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.



1. Introduction, Overview and Definitions Part I: Computational Geometry
and Graph Applications
2. Approximation Schemes for Minimum-Cost
k-Connectivity Problems in Geometric Graphs
3. Dilation and Detours in
Geometric Networks
4. TheWell-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 Colouring 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 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.
Scheduling Data Broadcasts on Wireless Channels: Exact Solutions and
Time-Optimal Solutions for Uniform Data and Heuristics for Non-Uniform Data
26. Strategies for Aggregating Time-discounted Information in Sensor Networks
27. Approximation and exact algorithms for optimally placing a limited
numberof storage nodes in a wireless sensor network
28. Approximation
Algorithms for the Primer Selection, PlantedMotif 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. A GSO based
Swarm Algorithm for Odor Source Localization in Turbulent Environments
34.
Color Quantization
35. Digital Reputation for Virtual Communities
36.
Approximation for Influence Maximization
37. Approximation and Heuristics for
Community Detection
Teofilo Gonzalez is a professor of computer science at the University of California, Santa Barbara.