Abstract
We introduce an alternating decomposition approach for solving global optimization problems on parallel computers. The method is based on decomposing moves in the state space into components which can be optimized concurrently. A number of features distinguish our approach from others which have been proposed in the literature. We report the results of applying the approach to energy minimization of Lennard-Jones clusters using simulated annealing. We find that the new approach is robust, has high parallel efficiency, and it can rapidly generate a good approximation to the molecular conformation.
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