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.

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  1. Benner, Peter; Bujanović, Zvonimir; Kürschner, Patrick; Saak, Jens: A numerical comparison of different solvers for large-scale, continuous-time algebraic Riccati equations and LQR problems (2020)
  2. Cerdán, J.; Guerrero, D.; Marín, J.; Mas, J.: Preconditioners for rank deficient least squares problems (2020)
  3. Chao, Zhen; Xie, Dexuan; Sameh, Ahmed H.: Preconditioners for nonsymmetric indefinite linear systems (2020)
  4. Chen, Jia-Qi; Huang, Zheng-Da: On the error estimate of the randomized double block Kaczmarz method (2020)
  5. Davis, Timothy A.; Duff, Iain S.; Nakov, Stojce: Design and implementation of a parallel Markowitz threshold algorithm (2020)
  6. Duff, Iain; Hogg, Jonathan; Lopez, Florent: A new sparse (LDL^T) solver using a posteriori threshold pivoting (2020)
  7. Fukaya, Takeshi; Kannan, Ramaseshan; Nakatsukasa, Yuji; Yamamoto, Yusaku; Yanagisawa, Yuka: Shifted Cholesky QR for computing the QR factorization of ill-conditioned matrices (2020)
  8. 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)
  9. Kalantzis, Vassilis: A spectral Newton-Schur algorithm for the solution of symmetric generalized eigenvalue problems (2020)
  10. Kim, Jongeun; Veremyev, Alexander; Boginski, Vladimir; Prokopyev, Oleg A.: On the maximum small-world subgraph problem (2020)
  11. Li, Chengliang; Ma, Changfeng: The inexact Euler-extrapolated block preconditioners for a class of complex linear systems (2020)
  12. Mukunoki, Daichi; Ogita, Takeshi: Performance and energy consumption of accurate and mixed-precision linear algebra kernels on GPUs (2020)
  13. Niu, Yu-Qi; Zheng, Bing: A greedy block Kaczmarz algorithm for solving large-scale linear systems (2020)
  14. Sashikumaar Ganesan, Manan Shah: SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods (2020) arXiv
  15. Tatsuoka, Fuminori; Sogabe, Tomohiro; Miyatake, Yuto; Zhang, Shao-Liang: Algorithms for the computation of the matrix logarithm based on the double exponential formula (2020)
  16. Xu, Taihua; Wang, Guoyin; Yang, Jie: Finding strongly connected components of simple digraphs based on granulation strategy (2020)
  17. Zheng, Yang; Fantuzzi, Giovanni; Papachristodoulou, Antonis; Goulart, Paul; Wynn, Andrew: Chordal decomposition in operator-splitting methods for sparse semidefinite programs (2020)
  18. Zhou, Qing; Benlic, Una; Wu, Qinghua: An opposition-based memetic algorithm for the maximum quasi-clique problem (2020)
  19. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  20. Aihara, Kensuke; Komeyama, Ryosuke; Ishiwata, Emiko: Variants of residual smoothing with a small residual gap (2019)

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