Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety

Abstract : Black-box optimization problems when the input space is a high-dimensional space or a function space appear in more and more applications. In this context, the methods available for finite-dimensional data do not apply. The aim is then to propose a general method for optimization involving dimension reduction techniques. Different dimension reduction basis are considered (including data-driven basis). The methodology is illustrated on simulated functional data. The choice of the different parameters, in particular the dimension of the approximation space, is discussed. The method is finally applied to a problem of nuclear safety.
Type de document :
Article dans une revue
Computational Statistics, Springer Verlag, A Paraître, 〈10.1007/s00180-017-0751-1〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01144628
Contributeur : Angelina Roche <>
Soumis le : mardi 17 novembre 2015 - 16:54:29
Dernière modification le : mardi 10 octobre 2017 - 19:42:56

Fichiers

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

Identifiants

Collections

Citation

Angelina Roche. Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety. Computational Statistics, Springer Verlag, A Paraître, 〈10.1007/s00180-017-0751-1〉. 〈hal-01144628v3〉

Partager

Métriques

Consultations de
la notice

182

Téléchargements du document

96