SDO : A Statistical Method for Global Optimization

An algorithm for nding global optima using statistical prediction is presented. Assuming a random function model, lower conndence bounds on predicted values are used for sequential selection of evaluation points and as a convergence criterion. Performance comparision with published results on several test functions indicate that the procedure is very eecient in nding the global optimum of a multimodal function, and in terminating with relatively few evaluations. The statistical computations involve linear algebra operations which are readily vectorized and is easy to adapt to parallel processing environments.

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