# Distributed generalized Nash equilibrium seeking in aggregative games on time-varying networks

## TL;DR

This paper introduces a fully-distributed algorithm for finding generalized Nash equilibria in aggregative games over dynamic networks, enabling agents to reach equilibrium without direct access to the aggregate decision.

## Contribution

It presents the first fully-distributed algorithm for generalized Nash equilibrium seeking in aggregative games on time-varying networks with partial information.

## Key findings

- Algorithm converges under monotone operator splitting framework
- Works on time-varying communication networks
- Handles partial-decision information scenarios

## Abstract

We design the first fully-distributed algorithm for generalized Nash equilibrium seeking in aggregative games on a time-varying communication network, under partial-decision information, i.e., the agents have no direct access to the aggregate decision. The algorithm is derived by integrating dynamic tracking into a projected pseudo-gradient algorithm. The convergence analysis relies on the framework of monotone operator splitting and the Krasnosel'skii-Mann fixed-point iteration with errors.