Parallel Combinatorial Optimization
Wiley, Oct 13, 2006 - Computers - 352 pages
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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Parallel BranchandBound Algorithms
Parallel Dynamic Programming
Parallel Branch and Cut
10 other sections not shown
AND-parallelism applications approach assigned best-first branch and cut branch-and-bound algorithms CHOLESKY clauses clique combinatorial optimization communication constraint constraint logic programming cooperation defined distributed domain DPLL efficiency evaluation evolutionary algorithms exact methods example explored framework function genetic algorithm global goal graph grid heuristic idle instances integer iteration knapsack linear load balancing logic programming lower bound LP relaxation machine MALLBA master process matrix memory metaheuristics MILP multiobjective MWKnap Nnde nodes NP/CG modules number of processors OpenMP operations optimal solution optimization problems OR-parallelism parallel algorithm parallel branch parallel computation parallel execution Pareto partitioning performance phase pool Prolog proposed rithms scalability SDPARA SDPARA-C search space search strategy search tree Section semidefinite programming sequential shared solve solver speedup subproblem subtree SYMPHONY tabu search Talbi tasks tion upper bound valid inequalities variables worker