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arXiv · 1811.04194

R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate

Jingzhao Zhang·Hongyi Zhang·Suvrit Sra
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Abstract

We study smooth stochastic optimization problems on Riemannian manifolds. Via adapting the recently proposed SPIDER algorithm \citep{fang2018spider} (a variance reduced stochastic method) to Riemannian manifold, we can achieve faster rate than known algorithms in both the finite sum and stochastic settings. Unlike previous works, by \emph{not} resorting to bounding iterate distances, our analysis yields curvature independent convergence rates for both the nonconvex and strongly convex cases.

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Jingzhao Zhang, Hongyi Zhang, Suvrit Sra. 2018-11-10. R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate. https://arxiv.org/abs/1811.04194

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