SuiteSparse

Suite Sparse Matrix Collection - SuiteSparse: a suite of sparse matrix packages. The SuiteSparse Matrix Collection (formerly known as the University of Florida Sparse Matrix Collection), is 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.


References in zbMATH (referenced in 38 articles )

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  1. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  2. Feng, Bo; Wu, Gang: On a new variant of Arnoldi method for approximation of eigenpairs (2022)
  3. Jiang, Xin; Vandenberghe, Lieven: Bregman primal-dual first-order method and application to sparse semidefinite programming (2022)
  4. Yang, Carl; Buluç, Aydın; Owens, John D.: GraphBLAST: a high-performance linear algebra-based graph framework on the GPU (2022)
  5. Bentbib, A. H.; El Ghomari, M.; Jbilou, K.; Reichel, L.: Shifted extended global Lanczos processes for trace estimation with application to network analysis (2021)
  6. Benzi, Michele: Some uses of the field of values in numerical analysis (2021)
  7. Garstka, Michael; Cannon, Mark; Goulart, Paul: COSMO: a conic operator splitting method for convex conic problems (2021)
  8. Hédin, Florent; Pichot, Géraldine; Ern, Alexandre: A hybrid high-order method for flow simulations in discrete fracture networks (2021)
  9. Tanneau, Mathieu; Anjos, Miguel F.; Lodi, Andrea: Design and implementation of a modular interior-point solver for linear optimization (2021)
  10. Wu, Feng; Zhang, Kailing; Zhu, Li; Hu, Jiayao: High-performance computation of the exponential of a large sparse matrix (2021)
  11. Bentbib, A. H.; El Ghomari, M.; Jbilou, K.: Extended nonsymmetric global Lanczos method for matrix function approximation (2020)
  12. Benzi, Michele; Fika, Paraskevi; Mitrouli, Marilena: Performance and stability of direct methods for computing generalized inverses of the graph Laplacian (2020)
  13. Fika, Paraskevi; Mitrouli, Marilena; Roupa, Paraskevi; Triantafyllou, Dimitrios: The e-MoM approach for approximating matrix functionals (2020)
  14. Miyajima, Shinya: Enclosing Moore-Penrose inverses (2020)
  15. Ota, Ryo; Hasegawa, Hidehiko: Predicting the convergence of BiCG method from grayscale matrix images (2020)
  16. Sugihara, Kota; Hayami, Ken; Zheng, Ning: Right preconditioned MINRES for singular systems. (2020)
  17. Yeung, Yu-Hong; Pothen, Alex; Crouch, Jessica: AMPS: real-time mesh cutting with augmented matrices for surgical simulations. (2020)
  18. Zheng, Qingqing; Xi, Yuanzhe; Saad, Yousef: Multicolor low-rank preconditioner for general sparse linear systems. (2020)
  19. Higham, Nicholas J.; Mary, Theo: A new preconditioner that exploits low-rank approximations to factorization error (2019)
  20. Lourenco, Christopher; Escobedo, Adolfo R.; Moreno-Centeno, Erick; Davis, Timothy A.: Exact solution of sparse linear systems via left-looking roundoff-error-free Lu factorization in time proportional to arithmetic work (2019)

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