| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Sao Paulo, Dept Comp Sci, Inst Math & Stat, BR-05508090 Sao Paulo - Brazil
[2] Univ Estadual Campinas, Inst Math Stat & Sci Comp, Dept Appl Math, Campinas, SP - Brazil
[3] Univ Simon Bolivar, Dept Comp Cient & Fis Estadist, Caracas 1080A - Venezuela
Total Affiliations: 3
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| Document type: | Review article |
| Source: | JOURNAL OF STATISTICAL SOFTWARE; v. 60, n. 3 SEP 2014. |
| Web of Science Citations: | 42 |
| Abstract | |
Over the last two decades, it has been observed that using the gradient vector as a search direction in large-scale optimization may lead to efficient algorithms. The effectiveness relies on choosing the step lengths according to novel ideas that are related to the spectrum of the underlying local Hessian rather than related to the standard decrease in the objective function. A review of these so-called spectral projected gradient methods for convex constrained optimization is presented. To illustrate the performance of these low-cost schemes, an optimization problem on the set of positive definite matrices is described. (AU) | |
| 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 |