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English(EN) Descending Everest, Part II: Oracle Complexity of Minimizing a Sum of Convex $(L_0,L_1)$-Smooth Functions

新研究论文详细介绍了用于凸函数最小化的 YETI 方法

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇题为“Descending Everest, Part II”的新研究论文探讨了具有特定平滑特性的凸函数和的最小化复杂度。该论文介绍了一种称为 YETI 的方法，该方法结合了加速近端和方差缩减技术。YETI 旨在以有限的 Oracle 调用次数实现目标精度，并在合成和泊松回归问题的实验中优于一阶方法。 AI

排序理由 在 arXiv 上发表的研究论文，详细介绍了一种新的优化方法。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新研究论文详细介绍了用于凸函数最小化的 YETI 方法

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在 arXiv 上发表的研究论文，详细介绍了一种新的优化方法。[lever_c_demoted from research: ic=1 ai=0.4]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Mikhail Rudomanov, Ivan Fomin, Alexander V. Gasnikov, Fedor S. Stonyakin ·

    攀登珠穆朗玛峰，第二部分：最小化凸 $(L_0,L_1)$-光滑函数之和的 Oracle 复杂度

    arXiv:2611.04171v2 Announce Type: replace-cross Abstract: We study the minimization of the average of $n$ convex $(L_0,L_1)$-smooth functions: the Hessian norm of each function is bounded by an affine function of its gradient norm. We assume known upper bounds $R$ on the distance…

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