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. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  2. Aurentz, Jared L.; Austin, Anthony P.; Benzi, Michele; Kalantzis, Vassilis: Stable computation of generalized matrix functions via polynomial interpolation (2019)
  3. Dobrian, Florin; Halappanavar, Mahantesh; Pothen, Alex; Al-Herz, Ahmed: A 2/3-approximation algorithm for vertex weighted matching in bipartite graphs (2019)
  4. Estrin, Ron; Orban, Dominique; Saunders, Michael: Euclidean-norm error bounds for SYMMLQ and CG (2019)
  5. Evangelopoulos, Xenophon; Brockmeier, Austin J.; Mu, Tingting; Goulermas, John Y.: Continuation methods for approximate large scale object sequencing (2019)
  6. Higham, Nicholas J.; Mary, Theo: A new preconditioner that exploits low-rank approximations to factorization error (2019)
  7. Hook, James; Pestana, Jennifer; Tisseur, Françoise; Hogg, Jonathan: Max-balanced Hungarian scalings (2019)
  8. Jia, Zhongxiao; Kang, Wenjie: A transformation approach that makes SPAI, PSAI and RSAI procedures efficient for large double irregular nonsymmetric sparse linear systems (2019)
  9. Paludetto Magri, Victor A.; Franceschini, Andrea; Janna, Carlo: A novel algebraic multigrid approach based on adaptive smoothing and prolongation for ill-conditioned systems (2019)
  10. Shen, Zhao-Li; Huang, Ting-Zhu; Carpentieri, Bruno; Wen, Chun; Gu, Xian-Ming; Tan, Xue-Yuan: Off-diagonal low-rank preconditioner for difficult PageRank problems (2019)
  11. Song, Liqiang; Yang, Wei Hong: A block Lanczos method for the extended trust-region subproblem (2019)
  12. Sun, Dong-Lin; Huang, Ting-Zhu; Carpentieri, Bruno; Jing, Yan-Fei: Flexible and deflated variants of the block shifted GMRES method (2019)
  13. Walteros, Jose L.; Veremyev, Alexander; Pardalos, Panos M.; Pasiliao, Eduardo L.: Detecting critical node structures on graphs: a mathematical programming approach (2019)
  14. Addam, Mohamed; Elbouyahyaoui, Lakhdar; Heyouni, Mohammed: On Hessenberg type methods for low-rank Lyapunov matrix equations (2018)
  15. Agullo, Emmanuel; Darve, Eric; Giraud, Luc; Harness, Yuval: Low-rank factorizations in data sparse hierarchical algorithms for preconditioning symmetric positive definite matrices (2018)
  16. Al-Mohy, Awad H.: A truncated Taylor series algorithm for computing the action of trigonometric and hyperbolic matrix functions (2018)
  17. Amini, S.; Toutounian, F.; Gachpazan, M.: The block CMRH method for solving nonsymmetric linear systems with multiple right-hand sides (2018)
  18. Anzt, Hartwig; Chow, Edmond; Dongarra, Jack: ParILUT -- a new parallel threshold ILU factorization (2018)
  19. Bai, Zhong-Zhi; Wu, Wen-Ting: On convergence rate of the randomized Kaczmarz method (2018)
  20. Bai, Zhong-Zhi; Wu, Wen-Ting: On relaxed greedy randomized Kaczmarz methods for solving large sparse linear systems (2018)

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