Mirror descent algorithms with productive/non-productive step switching achieve optimal convergence rates for bounded monotone operators and Lipschitz convex functional constraints in variational inequalities.
In proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain
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Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints
Mirror descent algorithms with productive/non-productive step switching achieve optimal convergence rates for bounded monotone operators and Lipschitz convex functional constraints in variational inequalities.