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| HAL : hal-00469622, version 1 |
| arXiv : 1004.0095 |
| Fiche détaillée |  Récupérer au format |
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| Convergence of a greedy algorithm for high-dimensional convex nonlinear problems |
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| Eric Cances 1, 2Virginie Ehrlacher 1, 2 |
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| (01/04/2010) |
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| In this article, we present a greedy algorithm based on a tensor product decomposition, whose aim is to compute the global minimum of a strongly convex energy functional. We prove the convergence of our method provided that the gradient of the energy is Lipschitz on bounded sets. The main interest of this method is that it can be used for high-dimensional nonlinear convex problems. We illustrate this method on a prototypical example for uncertainty propagation on the obstacle problem. |
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| 1 : | MICMAC (INRIA Paris - Rocquencourt) |
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Ecole des Ponts ParisTech – INRIA |
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| 2 : | Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS) |
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Ecole des Ponts ParisTech |
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| Domaine | : | Mathématiques/Analyse fonctionnelle |
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| Lien vers le texte intégral : |
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| http://fr.arXiv.org/abs/1004.0095 |
| hal-00469622, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00469622 | |
| oai:hal.archives-ouvertes.fr:hal-00469622 | |
| Contributeur : Virginie Ehrlacher | |
| Soumis le : Vendredi 2 Avril 2010, 09:04:36 | |
| Dernière modification le : Vendredi 2 Avril 2010, 09:04:36 | |
