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arXiv · 2111.13322

Random-reshuffled SARAH does not need a full gradient computations

Aleksandr Beznosikov·Martin Takáč
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Abstract

The StochAstic Recursive grAdient algoritHm (SARAH) algorithm is a variance reduced variant of the Stochastic Gradient Descent (SGD) algorithm that needs a gradient of the objective function from time to time. In this paper, we remove the necessity of a full gradient computation. This is achieved by using a randomized reshuffling strategy and aggregating stochastic gradients obtained in each epoch. The aggregated stochastic gradients serve as an estimate of a full gradient in the SARAH algorithm. We provide a theoretical analysis of the proposed approach and conclude the paper with numerical experiments that demonstrate the efficiency of this approach.

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Aleksandr Beznosikov, Martin Takáč. 2021-11-26. Random-reshuffled SARAH does not need a full gradient computations. https://doi.org/10.1007/s11590-023-02081-x

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