# Gradient Tracking: A Unified Approach to Smooth Distributed Optimization

arXiv: https://arxiv.org/abs/2202.09804 · HTML: https://mlsanity.com/paper/2202.09804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2022.

## Built on by (most cited first, 1 of 1)

- [Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems](https://mlsanity.com/paper/2307.07113/) (2023)

## Abstract (arXiv)

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.