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 308 articles )

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  1. Agullo, E.; Giraud, L.; Salas, P.; Zounon, M.: Interpolation-restart strategies for resilient eigensolvers (2016)
  2. Arrigo, Francesca; Benzi, Michele: Updating and downdating techniques for optimizing network communicability (2016)
  3. Arrigo, Francesca; Benzi, Michele: Edge modification criteria for enhancing the communicability of digraphs (2016)
  4. Arrigo, Francesca; Benzi, Michele; Fenu, Caterina: Computation of generalized matrix functions (2016)
  5. Azad, Ariful; Ballard, Grey; Buluç, Aydin; Demmel, James; Grigori, Laura; Schwartz, Oded; Toledo, Sivan; Williams, Samuel: Exploiting multiple levels of parallelism in sparse matrix-matrix multiplication (2016)
  6. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  7. Caliari, Marco; Kandolf, Peter; Ostermann, Alexander; Rainer, Stefan: The Leja method revisited: backward error analysis for the matrix exponential (2016)
  8. Cannataro, Begüm Şenses; Rao, Anil V.; Davis, Timothy A.: State-defect constraint pairing graph coarsening method for Karush-Kuhn-Tucker matrices arising in orthogonal collocation methods for optimal control (2016)
  9. Chen, Caihua; Liu, Yong-Jin; Sun, Defeng; Toh, Kim-Chuan: A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems (2016)
  10. Dufossé, Fanny; Uçar, Bora: Notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices (2016)
  11. Guan, Jinrui; Lu, Linzhang; Li, Ren-Cang; Shao, Rongxia: Self-corrective iterations (SCI) for generalized diagonally dominant matrices (2016)
  12. Gu, Xian-Ming; Huang, Ting-Zhu; Carpentieri, Bruno: BiCGCR2: A new extension of conjugate residual method for solving non-Hermitian linear systems (2016)
  13. Higham, Nicholas J.; Relton, Samuel D.: Estimating the largest elements of a matrix (2016)
  14. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the lighthouse framework (2016)
  15. Jiang, Bo; Liu, Ya-Feng; Wen, Zaiwen: $L_p$-norm regularization algorithms for optimization over permutation matrices (2016)
  16. Kalantzis, Vassilis; Li, Ruipeng; Saad, Yousef: Spectral Schur complement techniques for symmetric eigenvalue problems (2016)
  17. Khan, Arif; Pothen, Alex; Patwary, Md.Mostofa Ali; Satish, Nadathur Rajagopalan; Sundaram, Narayanan; Manne, Fredrik; Halappanavar, Mahantesh; Dubey, Pradeep: Efficient approximation algorithms for weighted $b$-matching (2016)
  18. Kopal, Jiří; Rozložník, Miroslav; Tuma, Miroslav: Factorized approximate inverses with adaptive dropping (2016)
  19. Lin, Lin; Saad, Yousef; Yang, Chao: Approximating spectral densities of large matrices (2016)
  20. Meng, Jing; Li, Hou-Biao; Jing, Yan-Fei: A new deflated block GCROT($m,k$) method for the solution of linear systems with multiple right-hand sides (2016)

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