English(EN) Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models

新的 SLSE-FRS 框架改进了线性模型估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-01 04:00

研究人员开发了一种名为“具有快速随机草图的序列最小二乘估计器”（SLSE-FRS）的新框架，用于估计大规模线性统计模型。该方法结合了草图求解（Sketch-and-Solve）和迭代草图（Iterative-Sketching）技术，通过解决越来越精确的草图子问题来迭代改进参数估计。数值实验表明，SLSE-FRS 在性能上优于当前的最新方法，如预条件共轭梯度（Preconditioned Conjugate Gradient）和迭代双草图（Iterative Double Sketching）。 AI

影响引入了一种新颖的算法方法，以提高线性统计模型估计的效率和精度。

排序理由该集群包含一篇详细介绍新统计估计框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Guan-Yu Chen, Dong-Yue Xie, Xi Yang · 2026-06-01 04:00

面向线性统计模型的快速随机投影序列最小二乘估计器

arXiv:2509.06856v2 Announce Type: replace Abstract: We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares Estimators with Fast Randomized Sketching (SLSE-FRS), which integrates Sketch-and-Solve…

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报道来源 [1]

面向线性统计模型的快速随机投影序列最小二乘估计器

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