Convergence properties of the expected improvement algorithm with fixed mean and covariance functions

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-almost all continuous functions, where P is the (prior) probability distribution induced by the Gaussian process.
Liste complète des métadonnées

https://hal-supelec.archives-ouvertes.fr/hal-00217562
Contributeur : Julien Bect <>
Soumis le : mercredi 19 mai 2010 - 21:18:34
Dernière modification le : jeudi 29 mars 2018 - 11:06:04
Document(s) archivé(s) le : jeudi 16 septembre 2010 - 12:20:29

Fichiers

note.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Emmanuel Vazquez, Julien Bect. Convergence properties of the expected improvement algorithm with fixed mean and covariance functions. Journal of Statistical Planning and Inference, Elsevier, 2010, 140 (11), pp.3088-3095. 〈10.1016/j.jspi.2010.04.018〉. 〈hal-00217562〉

Partager

Métriques

Consultations de la notice

523

Téléchargements de fichiers

967