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 LINPACK has been largely superceded by LAPACK, which has been designed to run efficiently on shared-memory, vector supercomputers.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:

References in zbMATH (referenced in 525 articles , 1 standard article )

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  1. Heinemann, Florian; Munk, Axel; Zemel, Yoav: Randomized Wasserstein barycenter computation: resampling with statistical guarantees (2022)
  2. 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)
  3. Drmač, Zlatko: Numerical methods for accurate computation of the eigenvalues of Hermitian matrices and the singular values of general matrices (2021)
  4. Fasi, Massimiliano; Higham, Nicholas J.: Matrices with tunable infinity-norm condition number and no need for pivoting in LU factorization (2021)
  5. Garcia, R. D. M.: Accurate spherical harmonics solutions for neutron transport problems in multi-region spherical geometry (2021)
  6. Riis, Nicolai André Brogaard; Dong, Yiqiu; Hansen, Per Christian: Computed tomography reconstruction with uncertain view angles by iteratively updated model discrepancy (2021)
  7. Zouraris, Georgios E.: Error estimation of the Besse relaxation scheme for a semilinear heat equation (2021)
  8. Hokpunna, Arpiruk; Misaka, Takashi; Obayashi, Shigeru; Wongwises, Somchai; Manhart, Michael: Finite surface discretization for incompressible Navier-Stokes equations and coupled conservation laws (2020)
  9. Avron, Haim; Druinsky, Alex; Toledo, Sivan: Spectral condition-number estimation of large sparse matrices. (2019)
  10. Bernal, Francisco: An implementation of Milstein’s method for general bounded diffusions (2019)
  11. Chen, Jian; Takeyama, Tomohide; O-Tani, Hideyuki; Fujita, Kohei; Motoyama, Hiroki; Hori, Muneo: Using high performance computing for liquefaction hazard assessment with statistical soil models (2019)
  12. Jing, Gangshan; Zhang, Guofeng; Lee, Heung Wing Joseph; Wang, Long: Angle-based shape determination theory of planar graphs with application to formation stabilization (2019)
  13. Lambers, James V.; Sumner, Amber C.: Explorations in numerical analysis (2019)
  14. Lang, Bruno: Efficient reduction of banded Hermitian positive definite generalized eigenvalue problems to banded standard eigenvalue problems (2019)
  15. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  16. Wu, Rongteng; Xie, Xiaohong: A heterogeneous parallel LU factorization algorithm based on a basic column block uniform allocation strategy (2019)
  17. Bertaccini, Daniele; Durastante, Fabio: Iterative methods and preconditioning for large and sparse linear systems with applications (2018)
  18. Conte, S. D.; de Boor, Carl: Elementary numerical analysis. An algorithmic approach. Updated with MATLAB (2018)
  19. 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)
  20. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Randomized complete pivoting for solving symmetric indefinite linear systems (2018)

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