Stochastic Push–Pull for Decentralized Nonconvex Optimization

Abstract

To understand the convergence behavior of the Push–Pull method for decentralized optimization with stochastic gradients (Stochastic Push–Pull), this paper presents a comprehensive analysis. Specifically, we first clarify the algorithm’s underlying assumptions, particularly those regarding the network structure and weight matrices. Then, to establish the convergence rate under smooth nonconvex objectives, we introduce a general analytical framework that not only encompasses a broad class of decentralized optimization algorithms, but also recovers or enhances several state-of-the-art results for distributed stochastic gradient tracking methods. A key highlight is the derivation of a sufficient condition under which the Stochastic Push–Pull algorithm achieves linear speedup, matching the scalability of centralized stochastic gradient methods. The condition has not been reported in prior Push–Pull literature. Extensive numerical experiments validate our theoretical findings, demonstrating the algorithm’s effectiveness and robustness across various decentralized optimization scenarios.