  In this paper we consider a distributed convex optimization problem over
time-varying undirected networks. We propose a dual method, primarily averaged
network dual ascent (PANDA), that is proven to converge R-linearly to the
optimal point given that the agents objective functions are strongly convex and
have Lipschitz continuous gradients. Like dual decomposition, PANDA requires
half the amount of variable exchanges per iterate of methods based on DIGing,
and can provide with practical improved performance as empirically
demonstrated.
