Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization

Abstract

Distributed optimization methods with probabilistic local updates have recently gained attention for their provable ability to communication acceleration. Nevertheless, this capability is effective only when the loss function is smooth and the network is sufficiently well-connected. In this paper, we propose the first linear convergent method MG-SKIP with probabilistic local updates for nonsmooth distributed optimization. Without any extra condition for the network connectivity, MG-SKIP allows for the multipleround gossip communication to be skipped in most iterations, while its iteration complexity is O(κ log 1 /ϵ) and communication complexity is only O( κ /(1−ρ) log 1 /ϵ), where κ is the condition number of the loss function and ρ reflects the connectivity of the network topology. To the best of our knowledge, MG-SKIP achieves the best communication complexity when the loss function has the smooth (strongly convex)+nonsmooth (convex) composite form.