Algorithm 915, SuiteSparseQR: Multifrontal multithreaded rank-revealing sparse QR factorization SuiteSparseQR is a sparse QR factorization package based on the multifrontal method. Within each frontal matrix, LAPACK and the multithreaded BLAS enable the method to obtain high performance on multicore architectures. Parallelism across different frontal matrices is handled with Intel’s Threading Building Blocks library. The symbolic analysis and ordering phase pre-eliminates singletons by permuting the input matrix A into the form [R11 R12; 0 A22] where R11 is upper triangular with diagonal entries above a given tolerance. Next, the fill-reducing ordering, column elimination tree, and frontal matrix structures are found without requiring the formation of the pattern of ATA. Approximate rank-detection is performed within each frontal matrix using Heath’s method. While Heath’s method is not always exact, it has the advantage of not requiring column pivoting and thus does not interfere with the fill-reducing ordering. For sufficiently large problems, the resulting sparse QR factorization obtains a substantial fraction of the theoretical peak performance of a multicore computer.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 17 articles )

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  1. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)
  2. Bujanović, Zvonimir; Kressner, Daniel: A block algorithm for computing antitriangular factorizations of symmetric matrices (2016)
  3. Everdij, Frank P.X.; Lloberas-Valls, Oriol; Simone, Angelo; Rixen, Daniel J.; Sluys, Lambertus J.: Domain decomposition and parallel direct solvers as an adaptive multiscale strategy for damage simulation in quasi-brittle materials (2016)
  4. Nürnberg, Robert; Sacconi, Andrea: A fitted finite element method for the numerical approximation of void electro-stress migration (2016)
  5. Arioli, Mario; Duff, Iain S.: Preconditioning linear least-squares problems by identifying a basis matrix (2015)
  6. Chandrasekaran, S.; Mhaskar, H.N.: A minimum Sobolev norm technique for the numerical discretization of PDEs (2015)
  7. Lei, Yuan: The inexact fixed matrix iteration for solving large linear inequalities in a least squares sense (2015)
  8. Batselier, Kim; Dreesen, Philippe; De Moor, Bart: A fast recursive orthogonalization scheme for the Macaulay matrix (2014)
  9. Batselier, Kim; Dreesen, Philippe; de Moor, Bart: The geometry of multivariate polynomial division and elimination (2013)
  10. Batselier, Kim; Dreesen, Philippe; De Moor, Bart: A geometrical approach to finding multivariate approximate LCMs and GCDs (2013)
  11. Buttari, Alfredo: Fine-grained multithreading for the multifrontal $QR$ factorization of sparse matrices (2013)
  12. Foster, Leslie V.; Davis, Timothy A.: Algorithm 933, reliable calculation of numerical rank, null space bases, pseudoinverse solutions, and basic solutions using SuiteSparseQR (2013)
  13. Jones, Daniel: Optimal stopping and the static forecasting problem (2013)
  14. Lian, Zhouhui; Godil, Afzal; Xiao, Jianguo: Feature-preserved 3D canonical form (2013) ioport
  15. Barrett, John W.; Garcke, Harald; Nürnberg, Robert: Parametric approximation of isotropic and anisotropic elastic flow for closed and open curves (2012)
  16. Barrett, John W.; Garcke, Harald; Nürnberg, Robert: Elastic flow with junctions: variational approximation and applications to nonlinear splines (2012)
  17. Davis, Timothy A.: Algorithm 915: SuiteSparseQR: multifrontal multithreaded rank-revealing sparse QR factorization (2011)