Stochastic generalized Nash equilibrium seeking under partial-decision information

Barbara Franci*, Sergio Grammatico

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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 neighbors. 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.

Original languageEnglish
Article number110101
Number of pages12
Publication statusPublished - 2022


  • Multi-agent systems
  • Nash games
  • Stochastic approximation


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