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outdated information received from some neighbors.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