| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] Univ Estadual Campinas, Dept Appl Math, Inst Math Stat & Sci Comp, Campinas, SP - Brazil
[2] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil
[3] Natl Univ La Plata, FCE, Dept Math, CONICET, RA-1900 La Plata, Bs As - Argentina
[4] Univ Sao Paulo, Inst Math & Stat, Sao Paulo - Brazil
Total Affiliations: 4
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| Document type: | Journal article |
| Source: | SIAM JOURNAL ON OPTIMIZATION; v. 22, n. 3, p. 1109-1135, 2012. |
| Web of Science Citations: | 40 |
| Abstract | |
We present two new constraint qualifications (CQs) that are weaker than the recently introduced relaxed constant positive linear dependence (RCPLD) CQ. RCPLD is based on the assumption that many subsets of the gradients of the active constraints preserve positive linear dependence locally. A major open question was to identify the exact set of gradients whose properties had to be preserved locally and that would still work as a CQ. This is done in the first new CQ, which we call the constant rank of the subspace component (CRSC) CQ. This new CQ also preserves many of the good properties of RCPLD, such as local stability and the validity of an error bound. We also introduce an even weaker CQ, called the constant positive generator (CPG), which can replace RCPLD in the analysis of the global convergence of algorithms. We close this work by extending convergence results of algorithms belonging to all the main classes of nonlinear optimization methods: sequential quadratic programming, augmented Lagrangians, interior point algorithms, and inexact restoration. (AU) | |
| FAPESP's process: | 06/53768-0 - Computational methods of optimization |
| Grantee: | José Mário Martinez Perez |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 10/19720-5 - Optimality conditions and inexact restoration |
| Grantee: | Gabriel Haeser |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 09/09414-7 - Penalty methods and optimality conditions |
| Grantee: | Gabriel Haeser |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |