Abstract
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.
Original language | Unknown |
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Pages (from-to) | 265-278 |
Journal | Computational Optimization And Applications |
Volume | 46 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2010 |