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 355 articles , 1 standard article )

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  1. Ahmadi-Asl, Salman; Beik, Fatemeh Panjeh Ali: Iterative algorithms for least-squares solutions of a quaternion matrix equation (2017)
  2. Aihara, Kensuke: Variants of the groupwise update strategy for short-recurrence Krylov subspace methods (2017)
  3. Bentbib, Abdeslem Hafid; Jbilou, Khalide; Sadek, El Mostafa: On some extended block Krylov based methods for large scale nonsymmetric Stein matrix equations (2017)
  4. Cerdán, J.; Marín, J.; Mas, J.: Low-rank updates of balanced incomplete factorization preconditioners (2017)
  5. Fika, Paraskevi; Mitrouli, Marilena: Aitken’s method for estimating bilinear forms arising in applications (2017)
  6. Gambhir, Arjun Singh; Stathopoulos, Andreas; Orginos, Kostas: Deflation as a method of variance reduction for estimating the trace of a matrix inverse (2017)
  7. Gupta, Anshul: Enhancing performance and robustness of ILU preconditioners by blocking and selective transposition (2017)
  8. Higham, Nicholas J.; Kandolf, Peter: Computing the action of trigonometric and hyperbolic matrix functions (2017)
  9. Imakura, Akira; Sakurai, Tetsuya: Block Krylov-type complex moment-based eigensolvers for solving generalized eigenvalue problems (2017)
  10. Ji, Hao; Li, Yaohang: A breakdown-free block conjugate gradient method (2017)
  11. Li, Jinchao; Andersen, Martin S.; Vandenberghe, Lieven: Inexact proximal Newton methods for self-concordant functions (2017)
  12. Lin, Lin: Localized spectrum slicing (2017)
  13. Lin, Lin: Randomized estimation of spectral densities of large matrices made accurate (2017)
  14. Marín, J.; Mas, J.; Guerrero, D.; Hayami, K.: Updating preconditioners for modified least squares problems (2017)
  15. Pichon, Gregoire; Faverge, Mathieu; Ramet, Pierre; Roman, Jean: Reordering strategy for blocking optimization in sparse linear solvers (2017)
  16. Pirova, Anna; Meyerov, Iosif; Kozinov, Evgeniy; Lebedev, Sergey: PMORSy: parallel sparse matrix ordering software for fill-in minimization (2017)
  17. Soodhalter, Kirk M.: Stagnation of block GMRES and its relationship to block FOM (2017)
  18. Zhu, Yao; Gleich, David F.; Grama, Ananth: Erasure coding for fault-oblivious linear system solvers (2017)
  19. Agullo, E.; Giraud, L.; Salas, P.; Zounon, M.: Interpolation-restart strategies for resilient eigensolvers (2016)
  20. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)

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