Linearly convergent decentralized consensus optimization over directed networks
Recently, there have been growing interests in solving distributed consensus optimization problems over directed networks that consist of multiple agents. In this paper, we develop a first order (gradient based) algorithm, referred to as Push DIGing, for this class of problems. To run Push DIGing, each agent in the network only needs to know its own out degree and employs a fixed step size. Under the strong convexity assumption, we prove that the introduced algorithm converges to the global minimizer at some R linear (geometric) rate as long as the nonnegative step size is no greater than some explicit bound.
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)