A double-loop variance-reduced stochastic forward-backward-forward method computes variational equilibria in risk-neutral stochastic games, with a.s. convergence, linear rates under strong monotonicity, and O(epsilon^-3) sample complexity under monotonicity.
Distributed GNE seeking under partial-decision information over networks via a doubly-augmented operator splitting approach,
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Complexity guarantees for risk-neutral generalized Nash equilibrium problems
A double-loop variance-reduced stochastic forward-backward-forward method computes variational equilibria in risk-neutral stochastic games, with a.s. convergence, linear rates under strong monotonicity, and O(epsilon^-3) sample complexity under monotonicity.