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Type: Artigo
Title: A second-order sequential optimality condition associated to the convergence of optimization algorithms
Author: Andreani, Roberto
Haeser, Gabriel
Ramos, Alberto
Silva, Paulo J. S.
Abstract: Sequential optimality conditions have recently played an important role on the analysis of the global convergence of optimization algorithms towards first-order stationary points, justifying their stopping criteria. In this article, we introduce a sequential optimality condition that takes into account second-order information and that allows us to improve the global convergence assumptions of several second-order algorithms, which is our main goal. We also present a companion constraint qualification that is less stringent than previous assumptions associated to the convergence of second-order methods, like the joint condition Mangasarian-Fromovitz and weak constant rank. Our condition is also weaker than the constant rank constraint qualification. This means that we can prove second-order global convergence of well-established algorithms even when the set of Lagrange multipliers is unbounded, which was a limitation of previous results based on Mangasarian-Fromovitz constraint qualification. We prove global convergence of well-known variations of the augmented Lagrangian and regularized sequential quadratic programming methods to second-order stationary points under this new weak constraint qualification
Subject: Programação não-linear
Condições de qualificação
Convergência global
Análise numérica
Nonlinear programming
Constraint qualifications
Global convergence
Numerical analysis
Country: Reino Unido
Editor: Oxford University Press
Rights: fechado
Identifier DOI: 10.1093/imanum/drw064
Date Issue: 2017
Appears in Collections:IMECC - Artigos e Outros Documentos

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