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. Ashcraft, Cleve; Buttari, Alfredo; Mary, Theo: Block low-rank matrices with shared bases: potential and limitations of the (BLR^2) format (2021)
  2. Baglama, James; Bella, Tom; Picucci, Jennifer: Hybrid iterative refined method for computing a few extreme eigenpairs of a symmetric matrix (2021)
  3. Bai, Zhong-Zhi; Wang, Lu; Wu, Wen-Ting: On convergence rate of the randomized Gauss-Seidel method (2021)
  4. Brezinski, C.; Redivo-Zaglia, M.: A survey of Shanks’ extrapolation methods and their applications (2021)
  5. Bujanovic, Zvonimir; Kressner, Daniel: Norm and trace estimation with random rank-one vectors (2021)
  6. 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)
  7. 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)
  8. Embree, Mark; Loe, Jennifer A.; Morgan, Ronald: Polynomial preconditioned Arnoldi with stability control (2021)
  9. Fasi, Massimiliano; Higham, Nicholas J.: Matrices with tunable infinity-norm condition number and no need for pivoting in LU factorization (2021)
  10. Gower, Robert M.; Molitor, Denali; Moorman, Jacob; Needell, Deanna: On adaptive sketch-and-project for solving linear systems (2021)
  11. Haddock, Jamie; Ma, Anna: Greed works: an improved analysis of sampling Kaczmarz-Motzkin (2021)
  12. Higham, Nicholas J.; Pranesh, Srikara: Exploiting lower precision arithmetic in solving symmetric positive definite linear systems and least squares problems (2021)
  13. Hokanson, Jeffrey M.; Constantine, Paul G.: A Lipschitz matrix for parameter reduction in computational science (2021)
  14. Huang, Jinzhi; Jia, Zhongxiao: On choices of formulations of computing the generalized singular value decomposition of a large matrix pair (2021)
  15. Ke, Yi-Fen: Convergence analysis on matrix splitting iteration algorithm for semidefinite linear complementarity problems (2021)
  16. Liu, Yong; Gu, Chuan-Qing: On greedy randomized block Kaczmarz method for consistent linear systems (2021)
  17. Manguoğlu, Murat; Volker, Mehrmann: A two-level iterative scheme for general sparse linear systems based on approximate skew-symmetrizers (2021)
  18. Niu, Yu-qi; Zheng, Bing: A new randomized Gauss-Seidel method for solving linear least-squares problems (2021)
  19. Oviedo, Harry; Dalmau, Oscar; Lara, Hugo: Two adaptive scaled gradient projection methods for Stiefel manifold constrained optimization (2021)
  20. Rezaei, Javad; Zare-Mirakabad, Fatemeh; MirHassani, Seyed Ali; Marashi, Sayed-Amir: EIA-CNDP: an exact iterative algorithm for critical node detection problem (2021)

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