Exponentially Converging Distributed Gradient Descent with Intermittent Communication via Hybrid Methods

Abstract

We present a hybrid systems framework for multi-agent optimization in which agents execute computations in continuous time and communicate in discrete time. The optimization algorithm is a hybrid version of parallelized coordinate descent. Agents implement a sample-and-hold strategy in which gradients are computed at communication times and held constant during flows between communications. Completeness of maximal solutions under these hybrid dynamics is established. Under assumptions of smoothness and strong convexity, we show that this system exponentially converges to the minimizer of an objective function. Simulation results illustrate this convergence rate.