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Sequential Constant Rank Constraint Qualifications for Nonlinear Semidefinite Programming with Algorithmic Applications

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Autor(es):
Andreani, Roberto ; Haeser, Gabriel ; Mito, Leonardo M. ; Ramirez, Hector
Número total de Autores: 4
Tipo de documento: Artigo Científico
Fonte: Set-Valued and Variational Analysis; v. 31, n. 1, p. 27-pg., 2023-03-01.
Resumo

We present new constraint qualification conditions for nonlinear semidefinite programming that extend some of the constant rank-type conditions from nonlinear programming. As an application of these conditions, we provide a unified global convergence proof of a class of algorithms to stationary points without assuming neither uniqueness of the Lagrange multiplier nor boundedness of the Lagrange multipliers set. This class of algorithms includes, for instance, general forms of augmented Lagrangian, sequential quadratic programming, and interior point methods. In particular, we do not assume boundedness of the dual sequence generated by the algorithm. The weaker sequential condition we present is shown to be strictly weaker than Robinson's condition while still implying metric subregularity. (AU)

Processo FAPESP: 17/18308-2 - Condições de otimalidade e algoritmos de segunda-ordem
Beneficiário:Gabriel Haeser
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 18/24293-0 - Métodos computacionais de otimização
Beneficiário:Sandra Augusta Santos
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 17/17840-2 - Estimativas de erro em otimização não linear
Beneficiário:Leonardo Makoto Mito
Modalidade de apoio: Bolsas no Brasil - Doutorado