Decentralized Finite-Sum Optimization over Time-Varying Networks
Abstract
We consider decentralized time-varying stochastic optimization problems where each of the functions held by the nodes has a finite sum structure. Such problems can be efficiently solved using variance reduction techniques. Our aim is to explore the lower complexity bounds (for communication and number of stochastic oracle calls) and find optimal algorithms. The paper studies strongly convex and nonconvex scenarios. To the best of our knowledge, variance reduced schemes and lower bounds for time-varying graphs have not been studied in the literature. For nonconvex objectives, we obtain lower bounds and develop an optimal method GT-PAGE. For strongly convex objectives, we propose the first decentralized time-varying variance-reduction method ADOM+VR and establish lower bound in this scenario, highlighting the open question of matching the algorithms complexity and lower bounds even in static network case.
Cite
@article{arxiv.2402.02490,
title = {Decentralized Finite-Sum Optimization over Time-Varying Networks},
author = {Dmitry Metelev and Savelii Chezhegov and Alexander Rogozin and Aleksandr Beznosikov and Alexander Sholokhov and Alexander Gasnikov and Dmitry Kovalev},
journal= {arXiv preprint arXiv:2402.02490},
year = {2025}
}
Comments
48 pages, 2 figures, 2 tables