SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient


Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takáč ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2613-2621, 2017.


In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framework for updating stochastic gradient estimates; when comparing to SAG/SAGA, SARAH does not require a storage of past gradients. The linear convergence rate of SARAH is proven under strong convexity assumption. We also prove a linear convergence rate (in the strongly convex case) for an inner loop of SARAH, the property that SVRG does not possess. Numerical experiments demonstrate the efficiency of our algorithm.

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