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New SGD method achieves sharp convergence rates for large-scale training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Centered Permutation Prefixes for Stochastic Gradient Descent (SGD) with Random Reshuffling. This technique aims to improve the efficiency of training on large datasets by optimizing how data is processed. The proposed method achieves sharp convergence rates, particularly for strongly convex functions with Lipschitz-continuous Hessians, and offers improvements even when components of the objective function are nonconvex. AI

IMPACT This research could lead to more efficient training of large machine learning models by improving optimization algorithms.

RANK_REASON The cluster contains a research paper detailing a new optimization method for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New SGD method achieves sharp convergence rates for large-scale training

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The cluster contains a research paper detailing a new optimization method for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiaxiang Li ·

    Centered Permutation Prefixes for SGD with Random Reshuffling: Sharp Rates, H\"older Geometry, and Composite Proximal Extensions

    arXiv:2609.04578v1 Announce Type: cross Abstract: We study stochastic gradient descent with random reshuffling for finite sums \[ F(x)=\frac1n\sum_{i=1}^n f_i(x). \] For fresh reshuffling with a constant component stepsize, if each $f_i$ has an $L$-Lipschitz gradient and the aver…

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