SEG-FFA achieves O~(1/K^{1/3}) convergence in convex-concave and O~(1/nK^4) in strongly-convex-strongly-concave finite-sum minimax problems, with lower bounds showing the gain over SGDA/SEG with random reshuffling.
Smooth monotone stochastic variational inequalities and saddle point problems: A survey
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Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements
SEG-FFA achieves O~(1/K^{1/3}) convergence in convex-concave and O~(1/nK^4) in strongly-convex-strongly-concave finite-sum minimax problems, with lower bounds showing the gain over SGDA/SEG with random reshuffling.