Accelerated Gradient Methods with Gradient Restart: Global Linear Convergence
Abstract
Gradient restarting has been shown to improve the numerical performance of accelerated gradient methods. This paper provides a mathematical analysis to understand these advantages. First, we establish global linear convergence guarantees for both the original and gradient restarted accelerated proximal gradient method when solving strongly convex composite optimization problems. Second, through analysis of the corresponding ordinary differential equation model, we prove the continuous trajectory of the gradient restarted Nesterov's accelerated gradient method exhibits global linear convergence for quadratic convex objectives, while the non-restarted version provably lacks this property by [Su, Boyd, and Cand\'es, \textit{J. Mach. Learn. Res.}, 2016, 17(153), 1-43].
Cite
@article{arxiv.2401.07672,
title = {Accelerated Gradient Methods with Gradient Restart: Global Linear Convergence},
author = {Chenglong Bao and Liang Chen and Jiahong Li and Zuowei Shen},
journal= {arXiv preprint arXiv:2401.07672},
year = {2025}
}