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 Specifically, assume that each agent only relies on outdated information received from some neighbors. Differently existing randomized and block-coordinate schemes, show convergence under heterogeneous delays, communication update frequencies. Consequently, our can be implemented without requiring any coordination between agents.