Mathematical Programming: Series A and B
USSR Computational Mathematics and Mathematical Physics
Mathematical Programming: Series A and B
Journal of Optimization Theory and Applications
A Newton method for a class of quasi-variational inequalities
Computational Optimization and Applications
On a Generalization of a Normal Map and Equation
SIAM Journal on Control and Optimization
A unifying geometric solution framework and complexity analysis for variational inequalities
Mathematical Programming: Series A and B
A Weak-to-Strong Convergence Principle for Fejé-Monotone Methods in Hilbert Spaces
Mathematics of Operations Research
SIAM Journal on Optimization
SIAM Journal on Optimization
SIAM Journal on Optimization
Comparison of Two Kinds of Prediction-Correction Methods for Monotone Variational Inequalities
Computational Optimization and Applications
An improved Goldstein's type method for a class of variant variational inequalities
Journal of Computational and Applied Mathematics
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The classical Goldstein's method has been well studied in the context of variational inequalities (VIs). In particular, it has been shown in the literature that the Goldstein's method works well for co-coercive VIs where the underlying mapping is co-coercive. In this paper, we show that the Goldstein's method can be extended to solve co-coercive variant variational inequalities (VVIs). We first show that when the Goldstein's method is applied to solve VVIs, the iterative scheme can be improved by identifying a refined step-size if the involved co-coercive modulus is known. By doing so, the allowable range of the involved scaling parameter ensuring convergence is enlarged compared to that in the context of VVIs with Lipschitz and strongly monotone operators. Then, we show that for such a VVI whose co-coercive modulus is unknown, the Goldstein's method is still convergent provided that an easily-implementable Armijo's type strategy of adjusting the scaling parameter self-adaptively is employed. Some numerical results are reported to verify that the proposed Goldstein's type methods are efficient for solving VVIs.