Conditional Accelerated Lazy Stochastic Gradient Descent
Abstract
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate $O(1/\epsilon^2)$ improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of (Hazan and Kale, 2012) with convergence rate $O(1/\epsilon^4)$.
Cite
Text
Lan et al. "Conditional Accelerated Lazy Stochastic Gradient Descent." International Conference on Machine Learning, 2017.Markdown
[Lan et al. "Conditional Accelerated Lazy Stochastic Gradient Descent." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/lan2017icml-conditional/)BibTeX
@inproceedings{lan2017icml-conditional,
title = {{Conditional Accelerated Lazy Stochastic Gradient Descent}},
author = {Lan, Guanghui and Pokutta, Sebastian and Zhou, Yi and Zink, Daniel},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {1965-1974},
volume = {70},
url = {https://mlanthology.org/icml/2017/lan2017icml-conditional/}
}