We propose a distributed method to solve a multi-agent optimization problem
with strongly convex cost function and equality coupling constraints. The
method is based on Nesterov's accelerated gradient approach and works over
stochastically time-varying communication networks. We consider the standard
assumptions of Nesterov's method and show that the sequence of the expected
dual values converge toward the optimal value with the rate of
O(1/k2). Furthermore, we provide a simulation study of solving an
optimal power flow problem with a well-known benchmark case.Comment: to appear at the 59th Conference on Decision and Contro