Linear and superlinear convergence of an inexact algorithm with proximal distances for variational inequality problems



	Linear and superlinear convergence of an inexact algorithm with proximal distances for variational inequality problems


	Fixed Point Theory, Volume 23, No. 1, 2022, 311-330, February 1st, 2022 DOI: 10.24193/fpt-ro.2022.1.20 Authors: E.A. Papa Quiroz and S. Cruzado Acuña 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. Key Words and Phrases: Variational inequalities, proximal distances, proximal point algorithms, quasimonotone and pseudomonotone mappings. 2010 Mathematics Subject Classification: 90C33, 47H10. Published on-line: February 1st, 2022. Abstract pdf Fulltext pdf Back to volume's table of contents