  We consider a network of agents that are cooperatively solving a global
optimization problem, where the objective function is the sum of privately
known local objective functions of the agents and the decision variables are
coupled via linear constraints. Recent literature focused on special cases of
this formulation and studied their distributed solution through either
subgradient based methods with O(1/sqrt(k)) rate of convergence (where k is the
iteration number) or Alternating Direction Method of Multipliers (ADMM) based
methods, which require a synchronous implementation and a globally known order
on the agents. In this paper, we present a novel asynchronous ADMM based
distributed method for the general formulation and show that it converges at
the rate O(1/k).
