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New Shuffling SARAH Algorithm Enhances Optimization Complexity Analysis

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Researchers have introduced Adjusted Shuffling SARAH, a new algorithm designed to improve the efficiency of optimization processes. This method incorporates dynamic gradient weighting and shuffling strategies within the SARAH framework. The algorithm is analyzed in two modes: an Exact Mode that matches theoretical guarantees for variance-reduced methods, and an Inexact Mode that uses mini-batch estimators for large-scale applications. Notably, the Inexact Mode achieves a total complexity independent of dataset size, offering superior scalability for large datasets compared to existing shuffling techniques. AI

RANK_REASON This is a research paper detailing a new algorithm for optimization. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Shuffling SARAH Algorithm Enhances Optimization Complexity Analysis

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This is a research paper detailing a new algorithm for optimization. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Duc Toan Nguyen, Trang H. Tran, Lam M. Nguyen ·

    Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting

    arXiv:2506.12444v2 Announce Type: replace-cross 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 algor…

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