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.
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Bibliographic citation
Papa, E., Cruzado, S. (2022). Linear and superlinear convergence of an inexact algorithm with proximal distances for variational inequality problems. Fixed Point Theory, 23(1). http://dx.doi.org/10.24193/fpt-ro.2022.1.20
Note
El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.