In this paper, a parallelasynchronous information algorithm for solving multidimensional Lipschitz global optimization problems, where times for evaluating the objective function can be different from point to point,...
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In this paper, a parallelasynchronous information algorithm for solving multidimensional Lipschitz global optimization problems, where times for evaluating the objective function can be different from point to point, is proposed. This method uses the nested optimization scheme and a new parallelasynchronous global optimization method for solving core univariate subproblems generated by the nested scheme. The properties of the scheme related to parallelcomputations are investigated. Global convergence conditions for the new method and theoretical conditions of speed up, which can be reached by using asynchronousparallelization in comparison with the pure sequential case, are established. Numerical experiments comparing sequential, synchronous, and asynchronous algorithms are also reported.
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