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English(EN) CAST: Canonical Approximate Schur Tree for Approximate Cholesky on Graphs

新的CAST方法改进了图乔里斯基分解

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种用于在图上构建近似乔里斯基分解的新方法CAST（规范近似舒尔树）。该技术旨在提高图数据工作负载中求解线性方程组的效率。CAST用加权随机生成树替换顶点消除过程中形成的稠密团，提供无偏更新，最大限度地减少局部误差贡献。CAST-ρ的扩展通过允许采样变异性和构建成本之间的可调权衡，进一步改进了这一点。 AI

影响 该方法可以提高各种AI应用中求解线性系统的效率，包括扩散估计和半监督学习。

排序理由 该集群包含一篇详细介绍图处理新算法方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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新的CAST方法改进了图乔里斯基分解

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该集群包含一篇详细介绍图处理新算法方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Meher Chaitanya, Cameron Musco, Aristides Gionis ·

    CAST: 图上的近似乔里斯基分解的规范近似舒尔树

    arXiv:2609.09255v1 Announce Type: new Abstract: Graph-data workloads such as diffusion estimation, ranking, semi-supervised learning, and network optimization often solve many Laplacian or symmetric diagonally dominant M-matrix (SDDM) systems with the same coefficient matrix. App…

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