Parallel Processing of Discrete Problems
Springer Science & Business Media, 1999 - 235 pàgines
In the past two decades, breakthroughs in computer technology have made a tremendous impact on optimization. In particular, availability of parallel computers has created substantial interest in exploring the use of parallel processing for solving discrete and global optimization problems. The chapters in this volume cover a broad spectrum of recent research in parallel processing of discrete and related problems. The topics discussed include distributed branch-and-bound algorithms, parallel genetic algorithms for large scale discrete problems, simulated annealing, parallel branch-and-bound search under limited-memory constraints, parallelization of greedy randomized adaptive search procedures, parallel optical models of computing, randomized parallel algorithms, general techniques for the design of parallel discrete algorithms, parallel algorithms for the solution of quadratic assignment and satisfiability problems. The book will be a valuable source of information to faculty, students and researchers in combinatorial optimization and related areas.
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DISTRIBUTED BRANCH AND BOUND ALGORITHMS FOR GLOBAL OPTIMIZATION
LARGESCALE STRUCTURED DISCRETE OPTIMIZATION via PARALLEL GENETIC ALGORITHMS
PUSHING THE LIMITS OF SOLVABLE QAP PROBLEMS USING PARALLEL PROCESSING IS NUGENT30 WITHIN REACH?
ON THE DESIGN OF PARALLEL DISCRETE ALGORITHMS FOR HIGH PERFORMANCE COMPUTING SYSTEMS
PARALLEL ALGORITHMS FOR SATISFIABILITY SAT TESTING
SEQUENTIAL AND PARALLEL BRANCHANDBOUND SEARCH UNDER LIMITEDMEMORY CONSTRAINTS
A PARALLEL GRASP FOR THE DATA ASSOCIATION MULTIDIMENSIONAL ASSIGNMENT PROBLEM
BASIC ALGORITHMS ON PARALLEL OPTICAL MODELS OF COMPUTING
RANDOMIZED PARALLEL ALGORITHMS
A PROBABILISTIC STUDY
Altres edicions - Mostra-ho tot
algorithm applications approach approximation architecture assignment balancing Boolean branch and bound cells combinatorial optimization communication complexity computing connected consider consists constant constraints construction corresponding cost defined described determined developed discrete discrete optimization discussed distributed domain edge efficient elements equal evaluation example execution Figure formulation function given global optimization graph graph partitioning implementation increase independent initial input instances integer iteration Lemma linear load lower bound Mathematics matrix maximum measurements memory methods minimizer nodes Note objective function obtained operations parallel parallel processing partition paths performance phase possible presented probability procedure processing processors programming quadratic randomized relaxation routing running SAT problem satisfiability Science selection sequential solution solving sorting space step structure takes techniques theorem Theory tion variables vertex weight