Using Problem Structure in Derivative-free Optimization

Résultats de recherche: Contribution à un journal/une revueArticle

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Résumé

Derivative-free unconstrained optimization is the class of optimization methods for which the derivatives of the objective function are unavailable. These methods are already well-studied, but are typically restricted to problems involving a small number of variables. The paper discusses how this restriction may be removed by the use the underlying problems structure, both in the case of pattern-search and interpolation methods. The focus is on partially separable objective function, but it is shown how Hessian sparsity, a weaker structure description, can also be used to advantage.
langue originaleAnglais
Pages (de - à)11-18
Nombre de pages8
journalSIOPT Views and News
Volume17
Numéro de publication1
étatNon publié - 2006

Empreinte digitale

Derivative-free Optimization
Objective function
Pattern Search
Unconstrained Optimization
Interpolation Method
Sparsity
Search Methods
Optimization Methods
Restriction
Derivative
Class

Citer ceci

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title = "Using Problem Structure in Derivative-free Optimization",
abstract = "Derivative-free unconstrained optimization is the class of optimization methods for which the derivatives of the objective function are unavailable. These methods are already well-studied, but are typically restricted to problems involving a small number of variables. The paper discusses how this restriction may be removed by the use the underlying problems structure, both in the case of pattern-search and interpolation methods. The focus is on partially separable objective function, but it is shown how Hessian sparsity, a weaker structure description, can also be used to advantage.",
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Using Problem Structure in Derivative-free Optimization. / Toint, Philippe.

Dans: SIOPT Views and News, Vol 17, Numéro 1, 2006, p. 11-18.

Résultats de recherche: Contribution à un journal/une revueArticle

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