Auxiliary problem principle extended to variational inequalities
Journal of Optimization Theory and Applications
Convergence of some algorithms for convex minimization
Mathematical Programming: Series A and B - Special issue: Festschrift in Honor of Philip Wolfe part II: studies in nonlinear programming
Approximations in proximal bundle methods and decomposition of convex programs
Journal of Optimization Theory and Applications
Family of perturbation methods for variational inequalities
Journal of Optimization Theory and Applications
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By combining the bundle idea with the auxiliary problem method we present an algorithm for solving a generalized variational inequality problem (GVIP). The proposed algorithm only requires the approximate function values and the approximate subgradients of the involved function instead of the exact ones at some points, which makes the algorithm easier to implement. In addition to that, the conditions imposed on the auxiliary functions are weaker than the preexisting results: convex is enough, not necessarily strongly convex. We also prove the weak convergence of the proposed algorithm under some conditions.