Research and Advances
Artificial Intelligence and Machine Learning

Anomalies in parallel branch-and-bound algorithms

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We consider the effects of parallelizing branch-and-bound algorithms by expanding several live nodes simultaneously. It is shown that it is quite possible for a parallel branch-and-bound algorithm using n2 processors to take more time than one using n1 processors, even though n1 < n2. Furthermore, it is also possible to achieve speed-ups that are in excess of the ratio n2/n1. Experimental results with the 0/1-Knapsack and Traveling Salesman problems are also presented.

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