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The Ethereal
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
May 20, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takรกฤ
arXiv ID
1705.07261
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.LG,
math.OC
Citations
98
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving empirical loss minimization for the case of nonconvex losses. We provide a sublinear convergence rate (to stationary points) for general nonconvex functions and a linear convergence rate for gradient dominated functions, both of which have some advantages compared to other modern stochastic gradient algorithms for nonconvex losses.
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