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. Bai, Zhong-Zhi; Wang, Lu; Wu, Wen-Ting: On convergence rate of the randomized Gauss-Seidel method (2021)
  2. Bujanovic, Zvonimir; Kressner, Daniel: Norm and trace estimation with random rank-one vectors (2021)
  3. Embree, Mark; Loe, Jennifer A.; Morgan, Ronald: Polynomial preconditioned Arnoldi with stability control (2021)
  4. Ke, Yi-Fen: Convergence analysis on matrix splitting iteration algorithm for semidefinite linear complementarity problems (2021)
  5. Liu, Yong; Gu, Chuan-Qing: On greedy randomized block Kaczmarz method for consistent linear systems (2021)
  6. Tajaddini, Azita; Wu, Gang; Saberi-Movahed, Farid; Azizizadeh, Najmeh: Two new variants of the simpler block GMRES method with vector deflation and eigenvalue deflation for multiple linear systems (2021)
  7. Urschel, John C.; Zikatanov, Ludmil T.: Discrete trace theorems and energy minimizing spring embeddings of planar graphs (2021)
  8. Wang, Qing-Wen; Xu, Xiangjian; Duan, Xuefeng: Least squares solution of the quaternion Sylvester tensor equation (2021)
  9. Yang, Xi: A geometric probability randomized Kaczmarz method for large scale linear systems (2021)
  10. Zou, Qinmeng; Magoulès, Frédéric: Fast gradient methods with alignment for symmetric linear systems without using Cauchy step (2021)
  11. Abdaoui, Ilias; Elbouyahyaoui, Lakhdar; Heyouni, Mohammed: The simpler block CMRH method for linear systems (2020)
  12. Azad, Ariful; Buluç, Aydin; Li, Xiaoye S.; Wang, Xinliang; Langguth, Johannes: A distributed-memory algorithm for computing a heavy-weight perfect matching on bipartite graphs (2020)
  13. 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)
  14. Cabral, Juan C.; Schaerer, Christian E.; Bhaya, Amit: Improving GMRES((m)) using an adaptive switching controller. (2020)
  15. 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)
  16. Carson, Erin Claire: An adaptive (s)-step conjugate gradient algorithm with dynamic basis updating. (2020)
  17. Carson, Erin; Higham, Nicholas J.; Pranesh, Srikara: Three-precision GMRES-based iterative refinement for least squares problems (2020)
  18. Cerdán, J.; Guerrero, D.; Marín, J.; Mas, J.: Preconditioners for rank deficient least squares problems (2020)
  19. Chao, Zhen; Xie, Dexuan; Sameh, Ahmed H.: Preconditioners for nonsymmetric indefinite linear systems (2020)
  20. Chen, Jia-Qi; Huang, Zheng-Da: On the error estimate of the randomized double block Kaczmarz method (2020)

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