Comparing two probabilistic models of the computational complexity of the branch and bound algorithm
We study two probabilistic models developed in order to predict the computational complexity of the branch and bound algorithm as well as its suitability for a parallelization based on the simultaneous exploration of all subproblems having a same common lower bound We show that both models, starting from different assumptions, yield asymptotically the same results but differ for small problems. Both models agree to predict a quick increase of the number of subproblems as a function of their lower bounds offering a convenient approach for parallelization of the branch and bound algorithm.
KeywordsBranch and bound algorithm combinatorial optimization complexity modelization parallel computing
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