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. Kim, Jongeun; Veremyev, Alexander; Boginski, Vladimir; Prokopyev, Oleg A.: On the maximum small-world subgraph problem (2020)
  2. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  3. Aihara, Kensuke; Komeyama, Ryosuke; Ishiwata, Emiko: Variants of residual smoothing with a small residual gap (2019)
  4. Aurentz, Jared L.; Austin, Anthony P.; Benzi, Michele; Kalantzis, Vassilis: Stable computation of generalized matrix functions via polynomial interpolation (2019)
  5. Bai, Zhong-Zhi; Wu, Wen-Ting: On partially randomized extended Kaczmarz method for solving large sparse overdetermined inconsistent linear systems (2019)
  6. Bellavia, Stefania; Gondzio, Jacek; Porcelli, Margherita: An inexact dual logarithmic barrier method for solving sparse semidefinite programs (2019)
  7. Bernaschi, Massimo; Carrozzo, Mauro; Franceschini, Andrea; Janna, Carlo: A dynamic pattern factored sparse approximate inverse preconditioner on graphics processing units (2019)
  8. Buttari, Alfredo; Orban, Dominique; Ruiz, Daniel; Titley-Peloquin, David: A tridiagonalization method for symmetric saddle-point systems (2019)
  9. Cipolla, Stefano; Di Fiore, Carmine; Zellini, Paolo: Low complexity matrix projections preserving actions on vectors (2019)
  10. Cui, Yiran; Morikuni, Keiichi; Tsuchiya, Takashi; Hayami, Ken: Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning (2019)
  11. Dobrian, Florin; Halappanavar, Mahantesh; Pothen, Alex; Al-Herz, Ahmed: A 2/3-approximation algorithm for vertex weighted matching in bipartite graphs (2019)
  12. Estrin, Ron; Orban, Dominique; Saunders, Michael: Euclidean-norm error bounds for SYMMLQ and CG (2019)
  13. Estrin, Ron; Orban, Dominique; Saunders, Michael A.: LNLQ: an iterative method for least-norm problems with an error minimization property (2019)
  14. Evangelopoulos, Xenophon; Brockmeier, Austin J.; Mu, Tingting; Goulermas, John Y.: Continuation methods for approximate large scale object sequencing (2019)
  15. Fung, Samy Wu; Ruthotto, Lars: An uncertainty-weighted asynchronous ADMM method for parallel PDE parameter estimation (2019)
  16. Goldenberg, Steven; Stathopoulos, Andreas; Romero, Eloy: A Golub-Kahan Davidson method for accurately computing a few singular triplets of large sparse matrices (2019)
  17. Grigori, Laura; Tissot, Olivier: Scalable linear solvers based on enlarged Krylov subspaces with dynamic reduction of search directions (2019)
  18. Herrmann, Julien; Özkaya, M. Yusuf; Uçar, Bora; Kaya, Kamer; Çatalyürek, ÜMit V.: Multilevel algorithms for acyclic partitioning of directed acyclic graphs (2019)
  19. Higham, Nicholas J.; Mary, Theo: A new approach to probabilistic rounding error analysis (2019)
  20. Higham, Nicholas J.; Mary, Theo: A new preconditioner that exploits low-rank approximations to factorization error (2019)

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