期刊
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
卷 46, 期 2, 页码 265-278出版社
SPRINGER
DOI: 10.1007/s10589-009-9283-0
关键词
Derivative-free optimization; Minimum Frobenius norm models; Direct search; Generalized pattern search; Search step; Data profiles
The goal of this paper is to show that the use of minimum Frobenius norm quadratic models can improve the performance of direct-search methods. The approach taken here is to maintain the structure of directional direct-search methods, organized around a search and a poll step, and to use the set of previously evaluated points generated during a direct-search run to build the models. The minimization of the models within a trust region provides an enhanced search step. Our numerical results show that such a procedure can lead to a significant improvement of direct search for smooth, piecewise smooth, and noisy problems.
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