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.
16th European Workshop on Reinforcement Learning (EWRL 2023).https://openreview.net/pdf?id=1EusBrDDrOK
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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.