Name
Title | ||
|---|---|---|
Forward–Backward algorithms for stochastic Nash equilibrium seeking in restricted strongly and strictly monotone games |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CDC45484.2021.9682852 | 2021 60th IEEE Conference on Decision and Control (CDC) |
DocType | ISSN | ISBN |
Conference | 0743-1546 | 978-1-6654-3660-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franci Barbara | 1 | 1 | 3.39 |
| Sergio Grammatico | 2 | 0 | 1.01 |