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.
Transactions on Machine Learning Research, 2835–8856 (2022)
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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.