English(EN) Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension

新算法弥合无导数问题的优化差距

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-14 16:10

研究人员开发了一种新的一维无导数随机凸优化算法。该算法实现了O(1/sqrt(T))的最优收敛速率，弥合了该特定优化领域理论上限和下限之间持续存在的差距。这项工作首次为这类问题提供了尖锐的速率保证，这类问题涉及在没有直接梯度信息的情况下，使用带噪声的函数评估来最小化凸函数。 AI

影响这项研究推进了优化领域的理论理解，可能影响依赖于高效优化技术的AI模型训练和其他计算密集型任务。

排序理由该集群包含一篇在arXiv上发表的学术论文，详细介绍了一种针对特定优化问题的新算法。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Alexandra Carpentier, Chlo\'e Rouyer, Alexandre Tsybakov, Arya Akhavan · 2026-07-15 04:00

一维无导数随机凸优化的Sharp Optimal算法

arXiv:2607.12938v1 Announce Type: cross Abstract: Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evaluations, a logarithmic gap has persisted between kn…
arXiv stat.ML TIER_1 English(EN) · Arya Akhavan · 2026-07-14 16:10

一维无导数随机凸优化的Sharp Optimal算法

Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evaluations, a logarithmic gap has persisted between known upper bounds and the $Ω(1/\sqrt{T})$ lower bou…