Infeasibility in augmented lagrangian methods

Author(s)

Leandro da Fonseca Prudente

• Document type: Doctoral Thesis
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica
• Defense date: 2012-04-05

Examining board members

José Mario Martínez Pérez; Roberto Andreani; Sandra Augusta Santos; Ernesto Julián Goldberg Birgin; Gabriel Haeser

• Advisor: José Mario Martínez Pérez

Abstract

Practical Nonlinear Programming algorithms may converge to infeasible points even when the problem to be solved is feasible. When this occurs, it is natural for the user to change the starting point and/or algorithmic parameters and reapply the method in an attempt to find a feasible and optimal solution. Thus, the ideal is that an algorithm is eficient not only in finding feasible solutions, but also in quickly detecting when it is fated to converge to an infeasible point. In pursuit of this goal, we present modifications of an algorithm based on Augmented Lagrangians so that, in the case of convergence to an infeasible point, the subproblems are solved with moderate tolerances and, even then, the global convergence properties are maintained. Numerical experiments are presented (AU)

• FAPESP's process: 09/00865-6 - Convergence, optimality conditions and properties on algorithms for non-linear programming problems
• Grantee: Leandro da Fonseca Prudente
• Support Opportunities: Scholarships in Brazil - Doctorate