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. Gu, Xian-Ming; Huang, Ting-Zhu; Yin, Guojian; Carpentieri, Bruno; Wen, Chun; Du, Lei: Restarted Hessenberg method for solving shifted nonsymmetric linear systems (2018)
  2. Hager, William W.; Hungerford, James T.; Safro, Ilya: A multilevel bilinear programming algorithm for the vertex separator problem (2018)
  3. Schlüter, Federico; Strappa, Yanela; Milone, Diego H.; Bromberg, Facundo: Blankets joint posterior score for learning Markov network structures (2018)
  4. Yang, Wangdong; Li, Kenli; Li, Keqin: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU (2018)
  5. Ahmadi-Asl, Salman; Beik, Fatemeh Panjeh Ali: Iterative algorithms for least-squares solutions of a quaternion matrix equation (2017)
  6. Aihara, Kensuke: Variants of the groupwise update strategy for short-recurrence Krylov subspace methods (2017)
  7. Bentbib, Abdeslem Hafid; Jbilou, Khalide; Sadek, El Mostafa: On some extended block Krylov based methods for large scale nonsymmetric Stein matrix equations (2017)
  8. Benzi, Michele; Uçar, Bora: Preconditioning techniques based on the Birkhoff-von Neumann decomposition (2017)
  9. Boutsidis, Christos; Drineas, Petros; Kambadur, Prabhanjan; Kontopoulou, Eugenia-Maria; Zouzias, Anastasios: A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix (2017)
  10. Cerdán, J.; Marín, J.; Mas, J.: Low-rank updates of balanced incomplete factorization preconditioners (2017)
  11. Fika, Paraskevi; Mitrouli, Marilena: Aitken’s method for estimating bilinear forms arising in applications (2017)
  12. Fischer, Thomas M.: On the algorithm by Al-Mohy and Higham for computing the action of the matrix exponential: a posteriori roundoff error estimation (2017)
  13. Gambhir, Arjun Singh; Stathopoulos, Andreas; Orginos, Kostas: Deflation as a method of variance reduction for estimating the trace of a matrix inverse (2017)
  14. Gatto, P.; Hesthaven, Jan S.: Efficient preconditioning of $hp$-FEM matrices by hierarchical low-rank approximations (2017)
  15. Greif, Chen; He, Shiwen; Liu, Paul: SYM-ILDL: Incomplete LDL$^\mathrm T$ factorization of symmetric indefinite and skew-symmetric matrices (2017)
  16. Gupta, Anshul: Enhancing performance and robustness of ILU preconditioners by blocking and selective transposition (2017)
  17. Han, Insu; Malioutov, Dmitry; Avron, Haim; Shin, Jinwoo: Approximating spectral sums of large-scale matrices using stochastic Chebyshev approximations (2017)
  18. Higham, Nicholas J.; Kandolf, Peter: Computing the action of trigonometric and hyperbolic matrix functions (2017)
  19. H. Li, G. C. Linderman, A. Szlam, K. P. Stanton, Y. Kluger, M. Tygert: Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis (2017)
  20. Imakura, Akira; Sakurai, Tetsuya: Block Krylov-type complex moment-based eigensolvers for solving generalized eigenvalue problems (2017)

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