Please use this identifier to cite or link to this item:
|Title:||A second-order sequential optimality condition associated to the convergence of optimization algorithms|
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|
Condições de qualificação
|Editor:||Oxford University Press|
|Appears in Collections:||IMECC - Artigos e Outros Documentos|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.