English(EN) Estimating condition number with Graph Neural Networks

图神经网络提供更快的矩阵条件数估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-25 04:00

研究人员开发了一种利用图神经网络（GNN）有效估计稀疏矩阵条件数的新颖方法。该方法引入了一种图特征构建技术，其复杂度相对于矩阵大小和非零元素呈线性关系。所提出的基于GNN的方案旨在预测条件数的某个分量或整个值，而无需显式矩阵求逆，与传统的数值估计方法相比，速度显著提升。相关软件已公开提供。 AI

影响这项研究通过提供一种更快的关键矩阵属性估计方法，有可能加速各个科学和工程领域的数值计算。

排序理由详细介绍使用图神经网络估计矩阵条件数新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Erin Carson, Xinye Chen · 2026-06-25 04:00

使用图神经网络估计条件数

arXiv:2603.10277v3 Announce Type: replace Abstract: In this paper, we propose a fast method for estimating the condition number of sparse matrices using graph neural networks (GNNs). For efficient deployment of GNNs, we introduce a graph feature construction with $\mathrm{O}(\mat…