Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An Augmented Lagrangian method for quasi-equilibrium problems

Full text
Author(s):
Bueno, L. F. [1] ; Haeser, G. [2] ; Lara, F. [3] ; Rojas, F. N. [4]
Total Authors: 4
Affiliation:
[1] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil
[2] Univ Sao Paulo, Dept Appl Math, Sao Paulo, SP - Brazil
[3] Univ Tarapaca, Dept Matemat, Arica - Chile
[4] Univ Sao Paulo, Inst Math & Stat, Dept Appl Math, Sao Paulo, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 76, n. 3, SI, p. 737-766, JUL 2020.
Web of Science Citations: 2
Abstract

In this paper, we propose an Augmented Lagrangian algorithm for solving a general class of possible non-convex problems called quasi-equilibrium problems (QEPs). We define an Augmented Lagrangian bifunction associated with QEPs, introduce a secondary QEP as a measure of infeasibility and we discuss several special classes of QEPs within our theoretical framework. For obtaining global convergence under a new weak constraint qualification, we extend the notion of an Approximate Karush-Kuhn-Tucker (AKKT) point for QEPs (AKKT-QEP), showing that in general it is not necessarily satisfied at a solution, differently from its counterpart in optimization. We study some particular cases where AKKT-QEP does hold at a solution, while discussing the solvability of the subproblems of the algorithm. We also present illustrative numerical experiments. (AU)

FAPESP's process: 13/05475-7 - Computational methods in optimization
Grantee:Sandra Augusta Santos
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 18/24293-0 - Computational methods in optimization
Grantee:Sandra Augusta Santos
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 17/18308-2 - Second-order optimality conditions and algorithms
Grantee:Gabriel Haeser
Support Opportunities: Regular Research Grants