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. 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. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  6. Caliari, Marco; Kandolf, Peter; Ostermann, Alexander; Rainer, Stefan: The Leja method revisited: backward error analysis for the matrix exponential (2016)
  7. 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)
  8. Chen, Caihua; Liu, Yong-Jin; Sun, Defeng; Toh, Kim-Chuan: A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems (2016)
  9. Dufossé, Fanny; Uçar, Bora: Notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices (2016)
  10. Guan, Jinrui; Lu, Linzhang; Li, Ren-Cang; Shao, Rongxia: Self-corrective iterations (SCI) for generalized diagonally dominant matrices (2016)
  11. Gu, Xian-Ming; Huang, Ting-Zhu; Carpentieri, Bruno: BiCGCR2: A new extension of conjugate residual method for solving non-Hermitian linear systems (2016)
  12. Kopal, Jiří; Rozložník, Miroslav; Tuma, Miroslav: Factorized approximate inverses with adaptive dropping (2016)
  13. Lin, Lin; Saad, Yousef; Yang, Chao: Approximating spectral densities of large matrices (2016)
  14. 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)
  15. Milyukova, Olga Yu.: Combination of numerical and structured approaches to the construction of a second-order incomplete triangular factorization in parallel preconditioning methods (2016)
  16. Napov, Artem; Notay, Yvan: An efficient multigrid method for graph Laplacian systems (2016)
  17. Pacull, François; Tromeur-Dervout, Damien: Preconditioning of the reduced system associated with the restricted additive Schwarz method (2016)
  18. Rashedi, Somaiyeh; Ebadi, Ghodrat; Birk, Sebastian; Frommer, Andreas: On short recurrence Krylov type methods for linear systems with many right-hand sides (2016)
  19. Reichel, Lothar; Rodriguez, Giuseppe; Tang, Tunan: New block quadrature rules for the approximation of matrix functions (2016)
  20. Shi, Zhanwen; Yang, Guanyu; Xiao, Yunhai: A limited memory BFGS algorithm for non-convex minimization with applications in matrix largest eigenvalue problem (2016)

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