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E-raamat: Parallel Combinatorial Optimization

Edited by (University of Lille, Villeneuve d'Aseq, France)
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In applied mathematics and computer science, combinatorial optimization algorithms solve problems that are believed to be hard in general, by exploring the usually large solution search space of the problem, then reducing its effective size and exploring it efficiently. Researchers from Europe, the Americas, and Japan explore exact algorithms, metaheuristics, hybrid approaches using both, and multi-objective optimization algorithms. They also consider frameworks and libraries that integrate parallel algorithms for combinatorial optimization. Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)

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.

Arvustused

"The target audience will learn a lot from the book, and I hope they will be inspired" (Computing Reviews.com, May 30, 2007)

Preface ix
Acknowledgments xi
Contributors xiii
1. Parallel Branch-and-Bound Algorithms
1(28)
T. Crainic, B. Lecun, C. Roucairol
2. Parallel Dynamic Programming
29(24)
F. Almeida, D. Gonzalez, I. Pelaez
3. Parallel Branch and Cut
53(50)
T. Ralphs
4. Parallel Semidefinite Programming and Combinatorial Optimization
103(20)
S.J. Benson
5. Parallel Resolution of the Satisfiability Problem: A Survey
123(26)
D. Singer
6. Parallel Metaheuristics: Algorithms and Frameworks
149(14)
N. Melab, E-G. Talbi, S. Cahon, E. Alba, G. Luque
7. Towards Parallel Design of Hybrids between Metaheuristics and Exact Methods
163(24)
M. Basseur L. Jourdan, E-G. Talbi
8. Parallel Exact Methods for Multiobjective Combinatorial Optimization
187(24)
C. Dhaenens, J. Lemesre, N. Melah, M. Mezmaz, E-G. Talbi
9. Parallel Primal-Dual Interior Point Methods for Semidefinite Programs
211(28)
M. Yamashita, K. Fujisawa, M. Fukuda, M. Kojima, K. Nakata
10. MW: A Software Framework for Combinatorial Optimization on Computational Grids 239(24)
W. Glankwamdee, T. Linderoth
11. Constraint Logic Programming on Multiple Processors 263(38)
I. Sakellariou, I. Vlahavas
12. Application of Parallel Metaheuristics to Optimization Problems in Telecommunications and Bioinformatics 301(26)
S.L. Martins, C. Ribeiro, I. Rosseti
Index 327


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.