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Random Reshuffling Proven to Outperform SGD in Optimization

By PulseAugur Editorial · [2 sources] ·

Researchers have theoretically proven that Random Reshuffling (RR) outperforms standard Stochastic Gradient Descent (SGD) in smooth convex optimization. Previously, RR was considered a heuristic despite its empirical success, with theoretical limitations restricting its stepsize and convergence rates. This new work establishes that RR dominates SGD under any reasonable stepsize and after any finite number of epochs, resolving a long-standing open question in the field. AI

IMPACT Provides a theoretical foundation for a widely used optimization technique, potentially leading to more efficient AI model training.

RANK_REASON Academic paper published on arXiv detailing theoretical advancements in optimization algorithms.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Random Reshuffling Proven to Outperform SGD in Optimization

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zijian Liu ·

    Random Reshuffling Dominates Stochastic Gradient Descent

    arXiv:2606.32005v1 Announce Type: cross Abstract: Stochastic Gradient Descent ($\textsf{SGD}$) is one of the most classical optimization algorithms with favorable theoretical guarantees, yet the practical implementation of $\textsf{SGD}$ differs subtly from its well-known form an…

  2. arXiv cs.LG TIER_1 English(EN) · Zijian Liu ·

    Random Reshuffling Dominates Stochastic Gradient Descent

    Stochastic Gradient Descent ($\textsf{SGD}$) is one of the most classical optimization algorithms with favorable theoretical guarantees, yet the practical implementation of $\textsf{SGD}$ differs subtly from its well-known form and is often referred to as Shuffling Stochastic Gra…

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