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How to Make the Gradient Mapping Small for Constrained Stochastic Min-Max Problems and Beyond
We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone variational inequalities. We focus on the case when suboptimality is measured in terms of the gradient mapping, also known as, forward-backward or natural
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