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arXiv · 2202.09804

Gradient Tracking: A Unified Approach to Smooth Distributed Optimization

Jingwang Li·Housheng Su
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Abstract

In this work, we study the classical distributed optimization problem over digraphs, where the objective function is a sum of smooth local functions. Inspired by the implicit tracking mechanism proposed in our earlier work, we develop a unified algorithmic framework from a pure primal perspective, i.e., UGT, which is essentially a generalized gradient tracking method and can unify most existing distributed optimization algorithms with constant step-sizes. It is proved that two variants of UGT can both achieve linear convergence if the global objective function is strongly convex. Finally, the performance of UGT is evaluated by numerical experiments.

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Jingwang Li, Housheng Su. 2022-02-20. Gradient Tracking: A Unified Approach to Smooth Distributed Optimization. https://arxiv.org/abs/2202.09804

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