Exploiting problem structure in pattern search methods for unconstrained optimization

Chris Price, Philippe Toint

Résultats de recherche: Contribution à un journal/une revueArticle

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Résumé

A direct search method for unconstrained optimization is described. The method makes use of any partial separability structure that the objective function may have. The method uses successively finer nested grids, and minimizes the objective function over each grid in turn. All grids are aligned with the coordinate directions, which allows the partial separability structure of the objective function to be exploited. This has two advantages: it reduces the work needed to calculate function values at the points required and it provides function values at other points as a free by-product. Numerical results show that using partial separability can dramatically reduce the number of function evaluations needed to minimize a function, in some cases allowing problems with thousands of variables to be solved. Results show that the algorithm is effective on strictly C 1 problems and on a class of non-smooth problems.
langue originaleAnglais
Pages (de - à)479-491
Nombre de pages13
journalOptimization Methods and Software
Volume21
Numéro de publication3
Les DOIs
étatPublié - 1 juin 2006

Empreinte digitale

Pattern Search
Unconstrained Optimization
Separability
Search Methods
Objective function
Grid
Partial
Value Function
Direct Search Method
Minimise
Evaluation Function
Strictly
Calculate
Numerical Results
Function evaluation
Byproducts

Citer ceci

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Exploiting problem structure in pattern search methods for unconstrained optimization. / Price, Chris; Toint, Philippe.

Dans: Optimization Methods and Software, Vol 21, Numéro 3, 01.06.2006, p. 479-491.

Résultats de recherche: Contribution à un journal/une revueArticle

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