Handbook of Approximation Algorithms and Metaheuristics : Contemporary and Emerging Applications, Volume 2 book cover
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Handbook of Approximation Algorithms and Metaheuristics
Contemporary and Emerging Applications, Volume 2





ISBN 9780367571597
Published June 30, 2020 by Chapman and Hall/CRC
796 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

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

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Biography

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