BLAS

Low-level utilities common to many mathematical software packages. Primarily the Fortran BLAS (Basic Linear Algebra Subroutines) collected together by level (1, 2 and 3) and precision (real, double, complex, double complex). Also includes specialized BLAS implementations, the PORT machine-dependent constant routines, and the MACHAR software for dynamically determining machine-dependent arithmetic properties


References in zbMATH (referenced in 496 articles )

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  1. Abdelfattah, Ahmad; Costa, Timothy; Dongarra, Jack; Gates, Mark; Haidar, Azzam; Hammarling, Sven; Higham, Nicholas J.; Kurzak, Jakub; Luszczek, Piotr; Tomov, Stanimire; Zounon, Mawussi: A set of batched basic linear algebra subprograms and LAPACK routines (2021)
  2. Bosner, Nela: Parallel reduction of four matrices to condensed form for a generalized matrix eigenvalue algorithm (2021)
  3. Bosner, Nela: Parallel Prony’s method with multivariate matrix pencil approach and its numerical aspects (2021)
  4. Li, Nan; Girard, Anouck; Kolmanovsky, Ilya: Chance-constrained controller state and reference governor (2021)
  5. Steel, Thijs; Camps, Daan; Meerbergen, Karl; Vandebril, Raf: A multishift, multipole rational QZ method with aggressive early deflation (2021)
  6. Tobias Schoch: wbacon: Weighted BACON algorithms for multivariate outlier nomination (detection) and robust linear regression (2021) not zbMATH
  7. Van Zee, Field G.; Parikh, Devangi N.; Geijn, Robert A. Van De: Supporting mixed-domain mixed-precision matrix multiplication within the BLIS framework (2021)
  8. Bollhöfer, Matthias; Schenk, Olaf; Janalik, Radim; Hamm, Steve; Gullapalli, Kiran: State-of-the-art sparse direct solvers (2020)
  9. Frison, Gianluca; Sartor, Tommaso; Zanelli, Andrea; Diehl, Moritz: The BLAS API of BLASFEO: optimizing performance for small matrices (2020)
  10. Iakymchuk, Roman; Barreda, Maria; Wiesenberger, Matthias; Aliaga, José I.; Quintana-Ortí, Enrique S.: Reproducibility strategies for parallel preconditioned conjugate gradient (2020)
  11. Ji, Hao; Mascagni, Michael; Li, Yaohang: Gaussian variant of Freivalds’ algorithm for efficient and reliable matrix product verification (2020)
  12. Liu, Weifeng: Highly-scalable, highly-performant and highly-practical sparse matrix computations: progress and challenges (2020)
  13. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  14. Van Zee, Field G.: Implementing high-performance complex matrix multiplication via the 1M method (2020)
  15. Yuan, Xinru; Huang, Wen; Absil, P.-A.; Gallivan, Kyle A.: Computing the matrix geometric mean: Riemannian versus Euclidean conditioning, implementation techniques, and a Riemannian BFGS method. (2020)
  16. Amestoy, Patrick R.; de la Kethulle de Ryhove, Sébastien; L’Excellent, Jean-Yves; Moreau, Gilles; Shantsev, Daniil V.: Efficient use of sparsity by direct solvers applied to 3D controlled-source EM problems (2019)
  17. Amestoy, Patrick R.; L’Excellent, Jean-Yves; Moreau, Gilles: On exploiting sparsity of multiple right-hand sides in sparse direct solvers (2019)
  18. Barlow, Jesse L.: Block modified Gram-Schmidt algorithms and their analysis (2019)
  19. Bosse, Torsten: (Almost) matrix-free solver for piecewise linear functions in abs-normal form. (2019)
  20. Bylina, Beata; Bylina, Jarosław: The parallel tiled WZ factorization algorithm for multicore architectures (2019)

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