Convergence properties of the expected improvement algorithm with fixed mean and covariance functions - CentraleSupélec Accéder directement au contenu
Article Dans Une Revue Journal of Statistical Planning and Inference Année : 2010

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

Résumé

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.
Fichier principal
Vignette du fichier
note.pdf (124.28 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-00217562 , version 1 (19-05-2010)

Identifiants

Citer

Emmanuel Vazquez, Julien Bect. Convergence properties of the expected improvement algorithm with fixed mean and covariance functions. Journal of Statistical Planning and Inference, 2010, 140 (11), pp.3088-3095. ⟨10.1016/j.jspi.2010.04.018⟩. ⟨hal-00217562⟩
367 Consultations
1802 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More