Error bounds for generalized vector inverse quasi-variational inequality Problems with point to set mappings

Abstract

The goal of this paper is further to study a kind of generalized vector inverse quasi-variational inequality problems and to obtain error bounds in terms of the residual gap function, the regularized gap function, and the global gap function by utilizing the relaxed monotonicity and Hausdorff Lipschitz continuity. These error bounds provide effective estimated distances between an arbitrary feasible point and the solution set of generalized vector inverse quasi-variational inequality problems.

Keywords

• generalized vector inverse quasi-variational inequality problems
• strong monotonicity
• relaxed monotonicity
• hausdorff lipschitz continuity
• residual gap function
• regularized gap function
• global gap function
• bi-mapping
• error bounds
• generalized f-projection operator