Using approximate secant equations in limited memory methods for multilevel unconstrained optimization

Serge Gratton; Vincent Malmedy; Philippe Toint

doi:10.1007/s10589-011-9393-3

Using approximate secant equations in limited memory methods for multilevel unconstrained optimization

Serge Gratton, Vincent Malmedy, Philippe Toint

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Résumé

The properties of multilevel optimization problems defined on a hierarchy of discretization grids can be used to define approximate secant equations, which describe the second-order behavior of the objective function. Following earlier work by Gratton and Toint (2009) we introduce a quasi-Newton method (with a linesearch) and a nonlinear conjugate gradient method that both take advantage of this new second-order information. We then present numerical experiments with these methods and formulate recommendations for their practical use. © Springer Science+Business Media, LLC 2011.

langue originale	Anglais
Pages (de - à)	967-979
Nombre de pages	13
journal	Computational Optimization and Applications
Volume	51
Numéro de publication	3
Les DOIs	https://doi.org/10.1007/s10589-011-9393-3
Etat de la publication	Publié - 1 avr. 2012

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10.1007/s10589-011-9393-3

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1 Actif

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
SARTENAER, A. & TOINT, P.
1/01/87 → …
Projet: Axe de recherche
Optimisation multi-échelle non-linéaire
SARTENAER, A., TOINT, P., Malmedy, V., Tomanos, D. & Weber Mendonca, M.
1/07/04 → 31/07/11
Projet: Recherche

Contient cette citation

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Using approximate secant equations in limited memory methods for multilevel unconstrained optimization

Résumé

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ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire

Optimisation multi-échelle non-linéaire

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