Random-reshuffled SARAH does not need a full gradient computations

• URL: http://arxiv.org/abs/2111.13322v2
• Date: Sun, 14 Jan 2024 12:30:15 GMT
• Title: Random-reshuffled SARAH does not need a full gradient computations
• Authors: Aleksandr Beznosikov and Martin Tak\'a\v{c}
• Abstract summary: The StochAstic Recursive grAdientritHm (SARAH) algorithm is a variance reduced variant of the Gradient Descent (SGD) algorithm. In this paper, we remove the necessity of a full gradient. The aggregated gradients serve as an estimate of a full gradient in the SARAH algorithm.
• Score: 61.85897464405715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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