| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] UNL, FCT, CMA, P-2829516 Caparica - Portugal
[2] UNL, FCT, Dept Matemat, P-2829516 Caparica - Portugal
[3] Univ Estadual Campinas, IMECC UNICAMP, Dept Appl Math, Rua Sergio Buarque Holanda, BR-13083859 Campinas, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 75, n. 1 OCT 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
We present a new algorithm for solving large-scale unconstrained optimization problems that uses cubic models, matrix-free subspace minimization, and secant-type parameters for defining the cubic terms. We also propose and analyze a specialized trust-region strategy to minimize the cubic model on a properly chosen low-dimensional subspace, which is built at each iteration using the Lanczos process. For the convergence analysis we present, as a general framework, a model trust-region subspace algorithm with variable metric and we establish asymptotic as well as complexity convergence results. Preliminary numerical results, on some test functions and also on the well-known disk packing problem, are presented to illustrate the performance of the proposed scheme when solving large-scale problems. (AU) | |
| FAPESP's process: | 13/05475-7 - Computational methods in optimization |
| Grantee: | Sandra Augusta Santos |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |