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 601 articles , 1 standard article )
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Sorted by year (- Zou, Qinmeng; Magoulès, Frédéric: Fast gradient methods with alignment for symmetric linear systems without using Cauchy step (2021)
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- Cambier, Léopold; Chen, Chao; Boman, Erik G.; Rajamanickam, Sivasankaran; Tuminaro, Raymond S.; Darve, Eric: An algebraic sparsified nested dissection algorithm using low-rank approximations (2020)
- Carson, Erin Claire: An adaptive (s)-step conjugate gradient algorithm with dynamic basis updating. (2020)
- Cerdán, J.; Guerrero, D.; Marín, J.; Mas, J.: Preconditioners for rank deficient least squares problems (2020)
- Chao, Zhen; Xie, Dexuan; Sameh, Ahmed H.: Preconditioners for nonsymmetric indefinite linear systems (2020)
- Chen, Jia-Qi; Huang, Zheng-Da: On the error estimate of the randomized double block Kaczmarz method (2020)
- Çuğu, İlke; Manguoğlu, Murat: A parallel multithreaded sparse triangular linear system solver (2020)
- Davis, Timothy A.; Duff, Iain S.; Nakov, Stojce: Design and implementation of a parallel Markowitz threshold algorithm (2020)
- Duff, Iain; Hogg, Jonathan; Lopez, Florent: A new sparse (LDL^T) solver using a posteriori threshold pivoting (2020)
- Frommer, Andreas; Lund, Kathryn; Szyld, Daniel B.: Block Krylov subspace methods for functions of matrices. II: Modified block FOM (2020)
- Fukaya, Takeshi; Kannan, Ramaseshan; Nakatsukasa, Yuji; Yamamoto, Yusaku; Yanagisawa, Yuka: Shifted Cholesky QR for computing the QR factorization of ill-conditioned matrices (2020)
- Gu, Xian-Ming; Huang, Ting-Zhu; Carpentieri, Bruno; Imakura, Akira; Zhang, Ke; Du, Lei: Efficient variants of the CMRH method for solving a sequence of multi-shifted non-Hermitian linear systems simultaneously (2020)
- Itoh, Shoji; Sugihara, Masaaki: Changing over stopping criterion for stable solving nonsymmetric linear equations by preconditioned conjugate gradient squared method (2020)
- Kalantzis, Vassilis: A spectral Newton-Schur algorithm for the solution of symmetric generalized eigenvalue problems (2020)
- Kim, Jongeun; Veremyev, Alexander; Boginski, Vladimir; Prokopyev, Oleg A.: On the maximum small-world subgraph problem (2020)
- Li, Chengliang; Ma, Changfeng: The inexact Euler-extrapolated block preconditioners for a class of complex linear systems (2020)
- Manguoğlu, Murat; Polizzi, Eric; Sameh, Ahmed H.: Parallel hybrid sparse linear system solvers (2020)
- Mukunoki, Daichi; Ogita, Takeshi: Performance and energy consumption of accurate and mixed-precision linear algebra kernels on GPUs (2020)