LINPACK

LINPACK is a collection of Fortran subroutines that analyze and solve linear equations and linear least-squares problems. The package solves linear systems whose matrices are general, banded, symmetric indefinite, symmetric positive definite, triangular, and tridiagonal square. In addition, the package computes the QR and singular value decompositions of rectangular matrices and applies them to least-squares problems. LINPACK uses column-oriented algorithms to increase efficiency by preserving locality of reference. LINPACK was designed for supercomputers in use in the 1970s and early 1980s. LINPACK has been largely superceded by LAPACK, which has been designed to run efficiently on shared-memory, vector supercomputers. (Source: http://www.netlib.org/linpack/)


References in zbMATH (referenced in 458 articles , 1 standard article )

Showing results 1 to 20 of 458.
Sorted by year (citations)

1 2 3 ... 21 22 23 next

  1. Lambers, James V.; Sumner, Amber C.: Explorations in numerical analysis (2019)
  2. Bertaccini, Daniele; Durastante, Fabio: Iterative methods and preconditioning for large and sparse linear systems with applications (2018)
  3. Conte, S. D.; de Boor, Carl: Elementary numerical analysis. An algorithmic approach. Updated with MATLAB (2018)
  4. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Tomov, Stanimire; Yamazaki, Ichitaro: The singular value decomposition: anatomy of optimizing an algorithm for extreme scale (2018)
  5. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Randomized complete pivoting for solving symmetric indefinite linear systems (2018)
  6. Anderson, Edward: Algorithm 978: Safe scaling in the level 1 BLAS (2017)
  7. Chen, Cheng; Fang, Jianbin; Tang, Tao; Yang, Canqun: LU factorization on heterogeneous systems: an energy-efficient approach towards high performance (2017)
  8. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  9. Echebest, N.; Schuverdt, M. L.; Vignau, R. P.: An inexact restoration derivative-free filter method for nonlinear programming (2017)
  10. Elmar Peise; Paolo Bientinesi: Algorithm 979: Recursive Algorithms for Dense Linear Algebra - The ReLAPACK Collection (2017)
  11. Gentle, James E.: Matrix algebra. Theory, computations and applications in statistics (2017)
  12. Liang, Hui; Chen, Xiaobo: A new multi-domain method based on an analytical control surface for linear and second-order mean drift wave loads on floating bodies (2017)
  13. Van Zee, Field G.; Smith, Tyler M.: Implementing high-performance complex matrix multiplication via the 3m and 4m methods (2017)
  14. Wang, Wei-Guo; Wei, Yimin: Mixed and componentwise condition numbers for matrix decompositions (2017)
  15. Abdelfattah, A.; Anzt, H.; Dongarra, J.; Gates, M.; Haidar, A.; Kurzak, J.; Luszczek, P.; Tomov, S.; Yamazaki, I.; YarKhan, A.: Linear algebra software for large-scale accelerated multicore computing (2016)
  16. Chen, Yuxin; Keyes, David; Law, Kody J. H.; Ltaief, Hatem: Accelerated dimension-independent adaptive metropolis (2016)
  17. Marras, Simone; Kelly, James F.; Moragues, Margarida; Müller, Andreas; Kopera, Michal A.; Vázquez, Mariano; Giraldo, Francis X.; Houzeaux, Guillaume; Jorba, Oriol: A review of element-based Galerkin methods for numerical weather prediction: finite elements, spectral elements, and discontinuous Galerkin (2016)
  18. Xue, Dingyü; Chen, YangQuan: Scientific computing with MATLAB (2016)
  19. Blesgen, Thomas: On rotation deformation zones for finite-strain Cosserat plasticity (2015)
  20. Grewal, Mohinder S.; Andrews, Angus P.: Kalman filtering. Theory and practice with MATLAB (2015)

1 2 3 ... 21 22 23 next