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 676 articles , 1 standard article )
Showing results 1 to 20 of 676.
Sorted by year (- Ashcraft, Cleve; Buttari, Alfredo; Mary, Theo: Block low-rank matrices with shared bases: potential and limitations of the (BLR^2) format (2021)
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- Brezinski, C.; Redivo-Zaglia, M.: A survey of Shanks’ extrapolation methods and their applications (2021)
- Bujanovic, Zvonimir; Kressner, Daniel: Norm and trace estimation with random rank-one vectors (2021)
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- Du, Kui; Sun, Xiao-Hui: Randomized double and triple Kaczmarz for solving extended normal equations (2021)
- Du, Yi-Shu; Hayami, Ken; Zheng, Ning; Morikuni, Keiichi; Yin, Jun-Feng: Kaczmarz-type inner-iteration preconditioned flexible GMRES methods for consistent linear systems (2021)
- Elbouyahyaoui, Lakhdar; Heyouni, Mohammed; Tajaddini, Azita; Saberi-Movahed, Farid: On restarted and deflated block FOM and GMRES methods for sequences of shifted linear systems (2021)
- Embree, Mark; Loe, Jennifer A.; Morgan, Ronald: Polynomial preconditioned Arnoldi with stability control (2021)
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- Garstka, Michael; Cannon, Mark; Goulart, Paul: COSMO: a conic operator splitting method for convex conic problems (2021)
- Gower, Robert M.; Molitor, Denali; Moorman, Jacob; Needell, Deanna: On adaptive sketch-and-project for solving linear systems (2021)
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- Higham, Nicholas J.; Pranesh, Srikara: Exploiting lower precision arithmetic in solving symmetric positive definite linear systems and least squares problems (2021)
- Hokanson, Jeffrey M.; Constantine, Paul G.: A Lipschitz matrix for parameter reduction in computational science (2021)
- Hrga, Timotej; Povh, Janez: \textttMADAM: a parallel exact solver for max-cut based on semidefinite programming and ADMM (2021)
- Huang, Jinzhi; Jia, Zhongxiao: On choices of formulations of computing the generalized singular value decomposition of a large matrix pair (2021)
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- Jacquelin, Mathias; Ng, Esmond G.; Peyton, Barry W.: Fast implementation of the traveling-salesman-problem method for reordering columns within supernodes (2021)