PANDA: A Dual Linearly Converging Method for Distributed Optimization over Time-Varying Undirected Graphs

Understand

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.