A PROXIMAL POINT ALGORITHM FOR A NEW CLASS OF GENERALIZED STRONGLY NONLINEAR QUASI-VARIATIONAL-LIKE INCLUSIONS

Abstract

In this paper, we study a new class of generalized strongly nonlinear quasi-variational-like inclusions and construct a new iterative algorithm with errors for solving the generalized strongly nonlinear quasi-variational-like inclusion by using the eta-proximal mapping technique introduced by Ding and Luo. We also discuss the convergence of the iterative sequence generated by the algorithm. The results presented in this paper extend and improve many known results in the literature.