Distributionally Robust Nash Equilibria via Variational Inequalities
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918163975766016 |
|---|---|
| author | Alizadeh, Zeinab Farsi, Azadeh Jalilzadeh, Afrooz |
| author_facet | Alizadeh, Zeinab Farsi, Azadeh Jalilzadeh, Afrooz |
| contents | Nash Equilibrium and its robust counterpart, Distributionally Robust Nash Equilibrium (DRNE), are fundamental problems in game theory with applications in economics, engineering, and machine learning. This paper addresses the problem of DRNE, where multiple players engage in a noncooperative game under uncertainty. Each player aims to minimize their objective against the worst-case distribution within an ambiguity set, resulting in a minimax structure. We reformulate the DRNE problem as a Variational Inequality (VI) problem, providing a unified framework for analysis and algorithm development. We propose a gradient descent-ascent type algorithm with convergence guarantee that effectively addresses the computational challenges of high-dimensional and nonsmooth objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17024 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributionally Robust Nash Equilibria via Variational Inequalities Alizadeh, Zeinab Farsi, Azadeh Jalilzadeh, Afrooz Optimization and Control Nash Equilibrium and its robust counterpart, Distributionally Robust Nash Equilibrium (DRNE), are fundamental problems in game theory with applications in economics, engineering, and machine learning. This paper addresses the problem of DRNE, where multiple players engage in a noncooperative game under uncertainty. Each player aims to minimize their objective against the worst-case distribution within an ambiguity set, resulting in a minimax structure. We reformulate the DRNE problem as a Variational Inequality (VI) problem, providing a unified framework for analysis and algorithm development. We propose a gradient descent-ascent type algorithm with convergence guarantee that effectively addresses the computational challenges of high-dimensional and nonsmooth objectives. |
| title | Distributionally Robust Nash Equilibria via Variational Inequalities |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.17024 |