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Random-reshuffled SARAH does not need a full gradient computations
Aleksandr Beznosikov · Martin Takac

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.

Author Information

Aleksandr Beznosikov (Moscow Institute of Physics and Technology)
Martin Takac (Mohamed bin Zayed University of Artificial Intelligence (MBZUAI))

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