Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.automatica.2021.110101 | Automatica |
Keywords | DocType | Volume |
Nash games,Stochastic approximation,Multi-agent systems | Journal | 137 |
Issue | ISSN | Citations |
1 | 0005-1098 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franci Barbara | 1 | 1 | 3.39 |
| Sergio Grammatico | 2 | 173 | 25.63 |