Distributed Heavy-Ball: A Generalization and Acceleration of First-Order Methods With Gradient Tracking

Abstract

We study distributed optimization to minimize a global objective that is a sum of smooth and strongly-convex local cost functions. Recently, several algorithms over undirected and directed graphs have been proposed that use a gradient tracking method to achieve linear convergence to the global minimizer. However, a connection between these different approaches has been unclear. In this paper, we first show that many of the existing first-order algorithms are in fact related with a simple state transformation, at the heart of which lies the $\mathcal{AB}$ algorithm. We then describe \textit{distributed heavy-ball}, denoted as $\mathcal{AB}m$, i.e., $\mathcal{AB}$ with momentum, that combines gradient tracking with a momentum term and uses nonidentical local step-sizes. By simultaneously implementing both row- and column-stochastic weights, $\mathcal{AB}m$ removes the conservatism in the related work due to doubly-stochastic weights or eigenvector estimation. $\mathcal{AB}m$ thus naturally leads to optimization and average-consensus over both undirected and directed graphs, casting a unifying framework over several well-known consensus algorithms over arbitrary strongly-connected graphs. We show that $\mathcal{AB}m$ has a global $R$-linear rate when the largest step-size is positive and sufficiently small. Following the standard practice in the heavy-ball literature, we numerically show that $\mathcal{AB}m$ achieves accelerated convergence especially when the objective function is ill-conditioned.