Distributed dual averaging for convex optimization under communication delays

In this paper we extend and analyze the distributed dual averaging algorithm [1] to handle communication delays and general stochastic consensus protocols. Assuming each network link experiences some fixed bounded delay, we show that distributed dual averaging converges and the error decays at a rate O(T -0.5 ) where T is the number of iterations. This bound is an improvement over [1] by a logarithmic factor in T for networks of fixed size. Finally, we extend the algorithm to the case of using general non-averaging consensus protocols. We prove that the bias introduced in the optimization can be removed by a simple correction that depends on the stationary distribution of the consensus matrix.