The Distributed Dual Ascent Algorithm is Robust to Asynchrony

The distributed dual ascent is an established algorithm to solve strongly convex multi-agent optimization problems with separable cost functions, in the presence of coupling constraints. In this letter, we study its asynchronous counterpart. Specifically, we assume that each agent only relies on the outdated information received from some...

Fully distributed Nash equilibrium seeking over time-varying communication networks with linear convergence rate

We design a distributed algorithm for learning Nash equilibria over time-varying communication networks in a partial-decision information scenario, where each agent can access its own cost function and local feasible set, but can only observe the actions of some neighbors. Our algorithm is based on projected pseudo-gradient dynamics, augmented...

Multi-Vehicle Automated Driving as a Generalized Mixed-Integer Potential Game

This paper considers the multi-vehicle automated driving coordination problem. We develop a distributed, hybrid decision-making framework for safe and efficient autonomous driving of selfish vehicles on multi-lane highways, where each dynamics is modeled as a mixed-logical–dynamical system. We formalize the coordination problem as a generalized...