We study optimization of finite sums of \emph{geodesically} smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sums have witnessed tremendous attention in the recent years, existing work is limited to vector space problems. We introduce \emph{Riemannian SVRG} (\rsvrg), a new variance reduced Riemannian optimization method.

We study optimization of finite sums of \emph{geodesically} smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sums have witnessed tremendous attention in the recent years, existing work is limited to vector space problems. We introduce \emph{Riemannian SVRG} (\rsvrg), a new variance reduced Riemannian optimization method.

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This paper addresses a topic that was proposed as future work in [32], and by design, much of its contents are adaptations of results found in [14] and [21]. Thus, the analysis is incremental. Nonetheless, the paper contributes a manifoldized algorithm and bridges a gap between convex optimization and Riemannian optimization. Furthermore, the paper provides a useful lemma to analyze Riemannian methods, which can have a lasting impact. My main concern with this paper is its presentation.

We study optimization of finite sums of geodesically smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sums have witnessed tremendous attention in the recent years, existing work is limited to vector space problems.

We study optimization of finite sums of \emph{geodesically} smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sums have witnessed tremendous attention in the recent years, existing work is limited to vector space problems. We introduce \emph{Riemannian SVRG} (\rsvrg), a new variance reduced Riemannian optimization method. Our analysis reveals that \rsvrg inherits advantages of the usual SVRG method, but with factors depending on curvature of the manifold that influence its convergence. To our knowledge, \rsvrg is the first \emph{provably fast} stochastic Riemannian method.

We study optimization of finite sums of \emph{geodesically} smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sums have witnessed tremendous attention in the recent years, existing work is limited to vector space problems. We introduce \emph{Riemannian SVRG} (\rsvrg), a new variance reduced Riemannian optimization method. We analyze \rsvrg for both geodesically \emph{convex} and \emph{nonconvex} (smooth) functions. Our analysis reveals that \rsvrg inherits advantages of the usual SVRG method, but with factors depending on curvature of the manifold that influence its convergence. To our knowledge, \rsvrg is the first \emph{provably fast} stochastic Riemannian method. Moreover, our paper presents the first non-asymptotic complexity analysis (novel even for the batch setting) for nonconvex Riemannian optimization. Our results have several implications; for instance, they offer a Riemannian perspective on variance reduced PCA, which promises a short, transparent convergence analysis.