# Stochastic generalized Nash equilibrium seeking under partial-decision information

arXiv: https://arxiv.org/abs/2011.05357 · HTML: https://mlsanity.com/paper/2011.05357/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems](https://mlsanity.com/paper/2204.03132/) (2022)
- [Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise](https://mlsanity.com/paper/2503.18391/) (2025)
- [Learning to Control Unknown Strongly Monotone Games](https://mlsanity.com/paper/2407.00575/) (2024)
- [First-order methods for Stochastic Variational Inequality problems with Function Constraints](https://mlsanity.com/paper/2304.04778/) (2023)
- [Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach](https://mlsanity.com/paper/2511.14048/) (2025)

## Abstract (arXiv)

We consider for the first time a stochastic generalized Nash equilibrium problem, i.e., with expected-value cost functions and joint feasibility constraints, under partial-decision information, meaning that the agents communicate only with some trusted neighbours. We propose several distributed algorithms for network games and aggregative games that we show being special instances of a preconditioned forward-backward splitting method. We prove that the algorithms converge to a generalized Nash equilibrium when the forward operator is restricted cocoercive by using the stochastic approximation scheme with variance reduction to estimate the expected value of the pseudogradient.