# Forward-Backward algorithms for stochastic Nash equilibrium seeking in restricted strongly and strictly monotone games

arXiv: https://arxiv.org/abs/2103.03593 · HTML: https://mlsanity.com/paper/2103.03593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Asymmetric regularization mechanism for GAN training with Variational Inequalities](https://mlsanity.com/paper/2601.13920/) (2026)

## Abstract (arXiv)

We study stochastic Nash equilibrium problems with expected valued cost functions whose pseudogradient satisfies restricted monotonicity properties which hold only with respect to the solution. We propose a forward-backward algorithm and prove its convergence under restricted strong monotonicity, restricted strict monotonicity and restricted cocoercivity of the pseudogradient mapping. To approximate the expected value, we use either a finite number of samples and a vanishing step size or an increasing number of samples with a constant step. Numerical simulations show that our proposed algorithm might be faster than the available algorithms.