A Linear Scalarization Proximal Point Method for Quasiconvex Multiobjective Minimization

Erik Alex Papa Quiroz, Hellena Christina Fernandes Apolinário, Kely Diana Villacorta, Paulo Roberto Oliveira

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

6 Citas (Scopus)

Resumen

In this paper, we propose a linear scalarization proximal point algorithm for solving lower semicontinuous quasiconvex multiobjective minimization problems. Under some natural assumptions and, using the condition that the proximal parameters are bounded, we prove the convergence of the sequence generated by the algorithm and, when the objective functions are continuous, we prove the convergence to a generalized critical point of the problem. Furthermore, for the continuously differentiable case we introduce an inexact algorithm, which converges to a Pareto critical point.

Idioma originalInglés
Páginas (desde-hasta)1028-1052
Número de páginas25
PublicaciónJournal of Optimization Theory and Applications
Volumen183
N.º3
DOI
EstadoPublicada - 1 dic. 2019

Nota bibliográfica

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© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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