Accelerated Distributed Nesterov Gradient Descent for smooth and strongly convex functions

This paper considers the distributed optimization problem over a network, where the objective is to optimize a global function formed by a sum of local functions, using only local computation and communication. We develop an Accelerated Distributed Nesterov Gradient Descent (Acc-DNGD) method for strongly-convex and smooth functions. We show that it achieves a linear convergence rate and analyze how the convergence rate depends on the condition number and the underlying graph structure.