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Parallel Combinatorial Optimization

ISBN: 978-0-471-72101-7
352 pages
October 2006
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This text provides an excellent balance of theory and application that enables you to deploy powerful algorithms, frameworks, and methodologies to solve complex optimization problems in a diverse range of industries. Each chapter is written by leading experts in the fields of parallel and distributed optimization. Collectively, the contributions serve as a complete reference to the field of combinatorial optimization, including details and findings of recent and ongoing investigations.
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Table of Contents




1. Parallel Branch-and-Bound Algorithms (T. Crainic, B. Lecun, C. Roucairol).

2. Parallel Dynamic Programming (F. Almeida, D. Gonzalez, I. Pelaez).

3. Parallel Branch and Cut (T. Ralphs).

4. Parallel Semidefinite Programming and Combinatorial Optimization (S. J. Benson).

5. Parallel Resolution of the Satisfiability Problem: A Survey (D. Singer).

6. Parallel Metaheuristics: Algorithms and Frameworks (N. Melab, E-G. Talbi, S. Cahon, E. Alba, G. Luque).

7. Towards Parallel Design of Hybrids between Metaheuristics and Exact Methods (M. Basseur, L. Jourdan, E-G. Talbi).

8. Parallel Exact Methods for Multiobjective Combinatorial Optimization (C. Dhaenens, J. Lemesre, N. Melab, M. Mezmaz, E-G. Talbi).

9. Parallel Primal-Dual Interior Point Methods for Semidefinite Programs (M. Yamashita, K. Fujisawa, M. Fukuda, M. Kojima, K. Nakata).

10. MW: A Software Framework for Combinatorial Optimization on Computational Grids (W. Glankwamdee, T. Linderoth).

11. Constraint Logic Programming on Multiple Processors (I. Sakellariou, I. Vlahavas).

12. Application of Parallel Metaheuristics to Optimization Problems in Telecommunications and Bioinformatics (S. L. Martins, C. Ribeiro, I. Rosseti).


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Author Information

EL-GHAZALI TALBI, PHD, is Professor in the Computer Science Laboratory of the University of Lille, France. His research interests include parallel algorithms for combinatorial optimization and their applications to generic and real-world problems. Dr. Talbi leads the OPAC (Parallel Cooperative Optimization) research team; is the scientific leader of the INRIA DOLPHIN project dealing with distributed multi-objective optimization; and is active in several research and industrial projects, publications, and international conferences in the field.
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"The target audience will learn a lot from the book, and I hope they will be inspired…" (Computing Reviews.com, May 30, 2007)
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