SparseMatrix

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.


References in zbMATH (referenced in 724 articles , 1 standard article )

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  1. Al-Herz, Ahmed; Pothen, Alex: A (2/3)-approximation algorithm for vertex-weighted matching (2022)
  2. Bai, Zhong-Zhi; Wang, Lu; Muratova, Galina V.: On relaxed greedy randomized augmented Kaczmarz methods for solving large sparse inconsistent linear systems (2022)
  3. Bianchi, Davide; Donatelli, Marco; Durastante, Fabio; Mazza, Mariarosa: Compatibility, embedding and regularization of non-local random walks on graphs (2022)
  4. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  5. Chen, Jia-Qi; Huang, Zheng-Da: On a fast deterministic block Kaczmarz method for solving large-scale linear systems (2022)
  6. Chen, Jia-Qi; Huang, Zheng-Da: A fast block coordinate descent method for solving linear least-squares problems (2022)
  7. Christoph W. Wagner; Sebastian Semper; Jan Kirchhof: fastmat: Efficient linear transforms in Python (2022) not zbMATH
  8. Cortinovis, Alice; Kressner, Daniel; Massei, Stefano: Divide-and-conquer methods for functions of matrices with banded or hierarchical low-rank structure (2022)
  9. de Loynes, Basile; Navarro, Fabien; Olivier, Baptiste: Localized Fourier analysis for graph signal processing (2022)
  10. Dufossé, Fanny; Kaya, Kamer; Panagiotas, Ioannis; Uçar, Bora: Scaling matrices and counting the perfect matchings in graphs (2022)
  11. Gnanasekaran, Abeynaya; Darve, Eric: Hierarchical orthogonal factorization: sparse square matrices (2022)
  12. Hallman, Eric; Troester, Devon: A multilevel approach to stochastic trace estimation (2022)
  13. Huang, Yakui; Dai, Yu-Hong; Liu, Xin-Wei; Zhang, Hongchao: On the acceleration of the Barzilai-Borwein method (2022)
  14. Itoh, Shoji: Improvement of preconditioned bi-Lanczos-type algorithms with residual norm minimization for the stable solution of systems of linear equations (2022)
  15. Jiang, Xiang-Long; Zhang, Ke; Yin, Jun-Feng: Randomized block Kaczmarz methods with (k)-means clustering for solving large linear systems (2022)
  16. Jiao, Xiangmin; Chen, Qiao: Approximate generalized inverses with iterative refinement for (\epsilon)-accurate preconditioning of singular systems (2022)
  17. Laeuchli, Jesse; Ramírez-Cruz, Yunior; Trujillo-Rasua, Rolando: Analysis of centrality measures under differential privacy models (2022)
  18. Lv, Zhonglu; Bao, Wendi; Li, Weiguo; Wang, Fang; Wu, Guoli: On extended Kaczmarz methods with random sampling and maximum-distance for solving large inconsistent linear systems (2022)
  19. Nigam, Nilima; Pollock, Sara: A simple extrapolation method for clustered eigenvalues (2022)
  20. Saberi-Movahed, Farid; Tajaddini, Azita; Heyouni, Mohammed; Elbouyahyaoui, Lakhdar: Some iterative approaches for Sylvester tensor equations. II: A tensor format of simpler variant of GCRO-based methods (2022)

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