• Corpus ID: 235658735

# The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence

@article{Liu2021TheCR,
title={The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence},
author={Daogao Liu and Zhou Lu},
journal={ArXiv},
year={2021},
volume={abs/2106.14588}
}
• Published 28 June 2021
• Computer Science
• ArXiv
Stochastic Gradient Descent (SGD) is among the simplest and most popular methods in optimization. The convergence rate for SGD has been extensively studied and tight analyses have been established for the running average scheme, but the sub-optimality of the final iterate is still not well-understood. Shamir and Zhang [2013] gave the best known upper bound for the final iterate of SGD minimizing non-smooth convex functions, which is O(log T/ √ T ) for Lipschitz convex functions and O(log T/T…

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