English(EN) A Parameter-Free Zeroth-Order Method with Covariance Matrix Adaptation and Effective Dimension

推出新的无参数二阶优化方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员推出了一种新的无参数二阶优化算法POEM-CMA，该算法专为梯度信息不易获得的问题而设计。该方法通过结合协方差矩阵对齐和有效维度概念，增强了原始POEM算法，从而能够在信息丰富的方向上进行更集中的采样。POEM-CMA在收敛速度上接近最优，并在实际应用中显示出优于现有方法的改进，尤其是在具有低秩结构的问题上，这已通过LibSVM数据集上的数值实验得到证实。 AI

影响这项研究可能导致更有效的黑盒问题优化技术，并可能影响难以获得梯度的AI模型训练。

排序理由该集群包含一篇详细介绍新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Alexander Sholokhov, Alexander Rogozin · 2026-10-02 04:00

一种无参数的零阶方法，具有协方差矩阵自适应和有效维度

arXiv:2609.38561v1 Announce Type: cross Abstract: Zeroth-order optimization methods are essential for solving black-box problems where gradient information is unavailable or expensive to compute. This paper presents POEM-CMA, a novel parameter-free stochastic zeroth-order algorit…

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一种无参数的零阶方法，具有协方差矩阵自适应和有效维度

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