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arXiv · 2506.12444

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting

Duc Toan Nguyen·Trang H. Tran·Lam M. Nguyen
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Abstract

In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling strategies into the recursive SARAH framework using a dynamic weighting mechanism to enhance exploration. We analyze the algorithm under two operating modes. First, we show that the Exact Mode matches the best-known theoretical guarantees for shuffling variance-reduced methods in both strongly convex and non-convex settings. Second, to address large-scale regimes, we introduce an Inexact Mode that utilizes mini-batch estimators. A key contribution of our work is proving that this Inexact Mode achieves a total complexity independent of the dataset size, making it significantly more scalable than existing shuffling methods when the sample size is large.

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Duc Toan Nguyen, Trang H. Tran, Lam M. Nguyen. 2025-06-14. Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting. https://arxiv.org/abs/2506.12444

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