S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization

TL;DR

This paper introduces S-DIGing, a novel distributed stochastic gradient tracking algorithm that efficiently solves large-scale convex optimization problems by approximating gradients through stochastic averaging, ensuring linear convergence under certain conditions.

Contribution

The paper proposes a new stochastic gradient tracking algorithm combining gradient tracking with stochastic averaging, suitable for large-scale distributed convex optimization.

Findings

Linear convergence to the global optimum under strong convexity and Lipschitz conditions.

Effective in large-scale logistic regression problems.

Theoretical convergence guarantees supported by numerical experiments.

Abstract

In this paper, we study convex optimization problems where agents of a network cooperatively minimize the global objective function which consists of multiple local objective functions. Different from most of the existing works, the local objective function of each agent is presented as the average of finite instantaneous functions. The intention of this work is to solve large-scale optimization problems where the local objective function is complicated and numerous. Integrating the gradient tracking algorithm with stochastic averaging gradient technology, we propose a novel distributed stochastic gradient tracking (termed as S-DIGing) algorithm. At each time instant, only one randomly selected gradient of a instantaneous function is computed and applied to approximate the gradient of local objection function. Based on a primal-dual interpretation of the S-DIGing algorithm, we show that the S-DIGing algorithm linearly converges to the global optimal solution when step-size lies in an explicit internal under the assumptions that the instantaneous functions are strongly convex and have Lipschitz-continuous gradient. Numerical experiments on the logistic regression problem are presented to demonstrate the practicability of the algorithm and correctness of the theoretical results.