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

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.