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 LINPACK has been largely superceded by LAPACK, which has been designed to run efficiently on shared-memory, vector supercomputers.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 512 articles , 1 standard article )

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  1. Avron, Haim; Druinsky, Alex; Toledo, Sivan: Spectral condition-number estimation of large sparse matrices. (2019)
  2. Bernal, Francisco: An implementation of Milstein’s method for general bounded diffusions (2019)
  3. 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)
  4. Gorobets, A. V.; Neĭman-zade, M. I.; Okunev, S. K.; Kalyakin, A. A.; Sukov, S. A.: Performance of Elbrus-8C processor in supercomputer CFD simulations (2019)
  5. Jing, Gangshan; Zhang, Guofeng; Lee, Heung Wing Joseph; Wang, Long: Angle-based shape determination theory of planar graphs with application to formation stabilization (2019)
  6. Lambers, James V.; Sumner, Amber C.: Explorations in numerical analysis (2019)
  7. Lang, Bruno: Efficient reduction of banded Hermitian positive definite generalized eigenvalue problems to banded standard eigenvalue problems (2019)
  8. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  9. Wu, Rongteng; Xie, Xiaohong: A heterogeneous parallel LU factorization algorithm based on a basic column block uniform allocation strategy (2019)
  10. Bertaccini, Daniele; Durastante, Fabio: Iterative methods and preconditioning for large and sparse linear systems with applications (2018)
  11. Conte, S. D.; de Boor, Carl: Elementary numerical analysis. An algorithmic approach. Updated with MATLAB (2018)
  12. 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)
  13. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Randomized complete pivoting for solving symmetric indefinite linear systems (2018)
  14. Gustafsson, Bertil: Scientific computing. A historical perspective (2018)
  15. Alvarez, Amaury C.; García, Galina C.; Sarkis, Marcus: The ultra weak variational formulation for the modified mild-slope equation (2017)
  16. Anderson, Edward: Algorithm 978: Safe scaling in the level 1 BLAS (2017)
  17. Chen, Cheng; Fang, Jianbin; Tang, Tao; Yang, Canqun: LU factorization on heterogeneous systems: an energy-efficient approach towards high performance (2017)
  18. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  19. Echebest, N.; Schuverdt, M. L.; Vignau, R. P.: An inexact restoration derivative-free filter method for nonlinear programming (2017)
  20. Elmar Peise; Paolo Bientinesi: Algorithm 979: Recursive Algorithms for Dense Linear Algebra - The ReLAPACK Collection (2017) not zbMATH

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