AI RESEARCH
Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
arXiv CS.LG
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ArXi:2506.12444v2 Announce Type: replace-cross 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.