SparseMatrix
The University of Florida Sparse Matrix Collection. 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, Mathematica, 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.
Keywords for this software
References in zbMATH (referenced in 319 articles )
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- Agullo, E.; Giraud, L.; Salas, P.; Zounon, M.: Interpolation-restart strategies for resilient eigensolvers (2016)
- Aminfar, AmirHossein; Ambikasaran, Sivaram; Darve, Eric: A fast block low-rank dense solver with applications to finite-element matrices (2016)
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- Arrigo, Francesca; Benzi, Michele; Fenu, Caterina: Computation of generalized matrix functions (2016)
- Azad, Ariful; Ballard, Grey; Buluç, Aydin; Demmel, James; Grigori, Laura; Schwartz, Oded; Toledo, Sivan; Williams, Samuel: Exploiting multiple levels of parallelism in sparse matrix-matrix multiplication (2016)
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- Caliari, Marco; Kandolf, Peter; Ostermann, Alexander; Rainer, Stefan: The Leja method revisited: backward error analysis for the matrix exponential (2016)
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- Chen, Caihua; Liu, Yong-Jin; Sun, Defeng; Toh, Kim-Chuan: A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems (2016)
- Dufossé, Fanny; Uçar, Bora: Notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices (2016)
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- Higham, Nicholas J.; Relton, Samuel D.: Estimating the largest elements of a matrix (2016)
- Hogg, Jonathan D.; Ovtchinnikov, Evgueni; Scott, Jennifer A.: A sparse symmetric indefinite direct solver for GPU architectures (2016)
- Imakura, Akira; Li, Ren-Cang; Zhang, Shao-Liang: Locally optimal and heavy ball GMRES methods (2016)
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