Conference: Solving Combinatorial Optimization Problems in Parallel, 1999]]>

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Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 201-231, 1996]]>Without Abstract

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 87-114, 1996]]>Conference: Solving Combinatorial Optimization Problems in Parallel, 1996]]>

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Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 1-6, 1996]]>Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 171-200, 1996]]>

this paper, we present effective mapping and load-balancing functions thatyield nearly linear performance on large systems, when the inherent parallelism ofthe given optimization problem is large enough. After giving an overview of techniquesfor dynamically mapping tree structured computations onto parallel computerarchitectures, we will present detailed results of some techniques found to be veryefficient even on large scale parallel computing ...

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 115-144, 1996]]>er than the cost of an optimal solution. In [6], Christofides gives a simpleapproximation algorithm for the Traveling Salesman Problem (TSP), whichproduces a tour of length at most a factor 1.5 higher than the cost of an optimaltour. Approximation algorithms are often quite fast, and it is indeed appealingthat a guarantee on the solution quality can be given. Still, ...

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 248-274, 1996]]>Many (parallel) branch and bound algorithms look very different from each other at first glance. They exploit, however, the same underlying computational model. This phenomenon can be used to define branch and bound algorithms in terms of a set of basic rules that are applied in a specific (predefined) order. In the sequential case, the specification of Mitten's rules ...

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 145-170, 1996]]>e assigned problem. Clearly the goal is to make &quot;small&quot; the probabilitythat the algorithm solution is incorrect.The other approach consists in giving a suitable probability distributionon the input space and then designing algorithms having an efficientcomplexity in the average-case according to the previously defined inputdistribution.There are at least two motivations in studying parallel ...

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 25-50, 1996]]>Without Abstract

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 232-247, 1996]]>Without Abstract

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 7-24, 1996]]>Without Abstract

Conference: Solving Combinatorial Optimization Problems in Parallel, pp. 51-86, 1996]]>Without Abstract

Conference: Solving Combinatorial Optimization Problems in Parallel]]>Conference: Solving Combinatorial Optimization Problems in Parallel]]>