LINEAR AND SUPERLINEAR CONVERGENCE OF AN INEXACT ALGORITHM WITH PROXIMAL DISTANCES FOR VARIATIONAL INEQUALITY PROBLEMS

E. A. Papa Quiroz; S. Cruzado Acuña

doi:10.24193/fpt-ro.2022.1.20

LINEAR AND SUPERLINEAR CONVERGENCE OF AN INEXACT ALGORITHM WITH PROXIMAL DISTANCES FOR VARIATIONAL INEQUALITY PROBLEMS

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Resumen

This paper introduces an inexact proximal point algorithm using proximal distances with linear and superlinear rate of convergence for solving variational inequality problems when the mapping is pseudomonotone or quasimonotone. This algorithm is new even for the monotone case and from the theoretical point of view the error criteria used improves recent works in the literature.

Idioma original	Inglés
Páginas (desde-hasta)	311-330
Número de páginas	20
Publicación	Fixed Point Theory
Volumen	23
N.º	1
DOI	https://doi.org/10.24193/fpt-ro.2022.1.20
Estado	Publicada - 1 feb. 2022

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© 2022, House of the Book of Science. All rights reserved.

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abstract = "This paper introduces an inexact proximal point algorithm using proximal distances with linear and superlinear rate of convergence for solving variational inequality problems when the mapping is pseudomonotone or quasimonotone. This algorithm is new even for the monotone case and from the theoretical point of view the error criteria used improves recent works in the literature.",

keywords = "Proximal distances, Proximal point algorithms, Quasimonotone and pseudomonotone mappings, Variational inequalities",

author = "{Papa Quiroz}, {E. A.} and {Cruzado Acu{\~n}a}, S.",

year = "2022",

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N2 - This paper introduces an inexact proximal point algorithm using proximal distances with linear and superlinear rate of convergence for solving variational inequality problems when the mapping is pseudomonotone or quasimonotone. This algorithm is new even for the monotone case and from the theoretical point of view the error criteria used improves recent works in the literature.

AB - This paper introduces an inexact proximal point algorithm using proximal distances with linear and superlinear rate of convergence for solving variational inequality problems when the mapping is pseudomonotone or quasimonotone. This algorithm is new even for the monotone case and from the theoretical point of view the error criteria used improves recent works in the literature.

KW - Proximal distances

KW - Proximal point algorithms

KW - Quasimonotone and pseudomonotone mappings

KW - Variational inequalities

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LINEAR AND SUPERLINEAR CONVERGENCE OF AN INEXACT ALGORITHM WITH PROXIMAL DISTANCES FOR VARIATIONAL INEQUALITY PROBLEMS

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