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

E. A. Papa Quiroz, S. Cruzado Acuña

Direction of Research, Innovation and Social Responsibility

Universidad Nacional Mayor de San Marcos

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	311-330
Number of pages	20
Journal	Fixed Point Theory
Volume	23
Issue number	1
DOIs	https://doi.org/10.24193/fpt-ro.2022.1.20
State	Published - 1 Feb 2022

Keywords

Proximal distances
Proximal point algorithms
Quasimonotone and pseudomonotone mappings
Variational inequalities

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10.24193/fpt-ro.2022.1.20

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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",

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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

Abstract

Keywords

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