The University of Florida Sparse Matrix Collection
The University of Florida Sparse Matrix Collection
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DOI：
10.1145/2049662.2049663
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发表时间：
2011-11-01
影响因子：
2.7
通讯作者：
Hu, Yifan
中科院分区：
文献类型：
--
作者：
Davis, Timothy A.;Hu, Yifan
我们描述了佛罗里达大学的稀疏矩阵集合，这是一组在实际应用中出现的大量且正在迅速增长的稀疏矩阵。该集合被数值线性代数社区广泛用于稀疏矩阵算法的开发和性能评估。它允许稳健和可重复的实验：稳健，因为人工生成的矩阵的性能结果可能具有误导性；可重复，因为矩阵经过管理并以多种格式公开提供。它的矩阵覆盖了广泛的领域，包括由潜在的2D或3D几何(如结构工程、计算流体动力学、模型降阶、电磁、半导体器件、热力学、材料、声学、计算机图形/视觉、机器人/运动学和其他离散化)以及那些通常不具有此类几何的问题(优化、电路模拟、经济和金融建模、理论和量子化学、化学过程模拟、数学和统计、电力网络和其他网络和图形)引起的问题。我们提供用于访问和管理集合的软件，这些软件来自MatLab(TM)、数学(TM)、Fortran和C，以及在线搜索功能。提供了矩阵的图形可视化，并提出了一种新的多级粗化方案来促进这一任务。
We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments: robust because performance results with artificially generated matrices can be misleading, and repeatable because matrices are curated and made publicly available in many formats. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs). We provide software for accessing and managing the Collection, from MATLAB(TM), Mathematica(TM), Fortran, and C, as well as an online search capability. Graph visualization of the matrices is provided, and a new multilevel coarsening scheme is proposed to facilitate this task.