PANDA: A Dual Linearly Converging Method for Distributed Optimization over Time-Varying Undirected Graphs
arXiv:1803.08328
Abstract
In this paper we consider a distributed convex optimization problem over time-varying networks. We propose a dual method that converges R-linearly to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. The proposed method requires half the amount of variable exchanges per iterate than methods based on DIGing, and yields improved practical performance as empirically demonstrated.
Submitted to the 57th IEEE Conference on Decision and Control (CDC) 2018