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Error bounds for generalized vector inverse quasi-variational inequality Problems with point to set mappings
Journal article   Open access  Peer reviewed

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

S. S. Chang, Salahuddin, M. Liu, X. R. Wang, J. F. Tang, Department of Mathematics, Yibin University, Yibin, Sichuan, China and Salahuddin Salahuddin
AIMS mathematics, Vol.6(2), pp.1800-1815
01/01/2021
DOI: https://doi.org/10.3934/math.2021108

Abstract

bi-mapping error bounds generalized f-projection operator generalized vector inverse quasi-variational inequality problems global gap function hausdorff lipschitz continuity regularized gap function relaxed monotonicity residual gap function strong monotonicity
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.
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https://doi.org/10.3934/math.2021108View
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