Approximating Hessians in multilevel unconstrained optimization

Vincent Malmedy, Philippe Toint

Résultats de recherche: Contribution à un journal/une revueArticle

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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 behaviour 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.
langue originaleAnglais
Pages (de - à)1-22
Nombre de pages22
journalComputational Optimization and Applications
Volume50
Numéro de publication1
étatPublié - 2011

Empreinte digitale

Conjugate gradient method
Unconstrained Optimization
Newton-Raphson method
Quasi-Newton Method
Line Search
Conjugate Gradient Method
Chord or secant line
Recommendations
Objective function
Discretization
Experiments
Numerical Experiment
Optimization Problem
Grid
Hierarchy

Citer ceci

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Approximating Hessians in multilevel unconstrained optimization. / Malmedy, Vincent; Toint, Philippe.

Dans: Computational Optimization and Applications, Vol 50, Numéro 1, 2011, p. 1-22.

Résultats de recherche: Contribution à un journal/une revueArticle

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