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arXiv · 1703.05840

Conditional Accelerated Lazy Stochastic Gradient Descent

Guanghui Lan·Sebastian Pokutta·Yi Zhou·Daniel Zink
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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 $O\left(\frac{1}{\varepsilon^2}\right)$ improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan and Kale [2012] with convergence rate $O\left(\frac{1}{\varepsilon^4}\right)$.

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Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink. 2017-03-16. Conditional Accelerated Lazy Stochastic Gradient Descent. https://arxiv.org/abs/1703.05840

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