Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149092 | Journal of Statistical Planning and Inference | 2010 | 8 Pages |
Abstract
This paper deals with the convergence of the expected improvement algorithm, a popular global optimization algorithm based on a Gaussian process model of the function to be optimized. The first result is that under some mild hypotheses on the covariance function k of the Gaussian process, the expected improvement algorithm produces a dense sequence of evaluation points in the search domain, when the function to be optimized is in the reproducing kernel Hilbert space generated by k . The second result states that the density property also holds for P-almostP-almost all continuous functions, where P is the (prior) probability distribution induced by the Gaussian process.
Keywords
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Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Emmanuel Vazquez, Julien Bect,