Presents a stochastic extragradient algorithm for variational inequalities with Markovian noise, proving convergence under L-Lipschitzness, strong monotonicity, and noise bounded only at the optimum, plus experiments on mixing time.
Stochastic variance reduc- tion methods for saddle-point problems.Advances in Neural Information Processing Systems, 29
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Methods for Solving Variational Inequalities with Markovian Stochasticity
Presents a stochastic extragradient algorithm for variational inequalities with Markovian noise, proving convergence under L-Lipschitzness, strong monotonicity, and noise bounded only at the optimum, plus experiments on mixing time.