| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Estadual Campinas, Dept Appl Math, IMECC UNICAMP, Rua Sergio Buarque de Holanda 651, BR-13083859 Campinas, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 81, n. 1 OCT 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
We present a new algorithm for large-scale unconstrained minimization that, at each iteration, minimizes, approximately, a quadratic model of the objective function plus a regularization term, not necessarily based on a norm. We prove convergence assuming only gradient continuity and complexity results assuming Lipschitz conditions. For solving the subproblems in the case of regularizations based on the 3-norm, we introduce a new method that quickly obtains the approximate solutions required by the theory. We present numerical experiments. (AU) | |
| FAPESP's process: | 18/24293-0 - 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 |