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新优化方法实现凸有限和问题的近最优复杂度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的优化方法，用于光滑凸有限和问题，实现了 \tilde{\mathcal{O}}(n + n^{5/7} \epsilon^{-2/7}) 的复杂度。该方法是一种泰勒修正的惰性牛顿方法，通过利用个体光滑性和凸性假设，改进了先前的工作。新算法的性能在对数因子范围内匹配了该更强假设的既定下界。 AI

影响 这项研究通过改进优化算法，可能导致更高效的机器学习模型训练。

排序理由 该集群包含一篇详细介绍新优化算法及其理论复杂度的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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新优化方法实现凸有限和问题的近最优复杂度

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该集群包含一篇详细介绍新优化算法及其理论复杂度的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xinliang Zhang, Lesi Chen, Zonghan Li, Jingzhao Zhang ·

    Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness

    arXiv:2610.42040v1 Announce Type: cross Abstract: We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components. Very recently, Tsedenov \& Kovalev (2026) showed that the minimax optimal complexity under averaged smoot…

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