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 LINPACK has been largely superceded by LAPACK, which has been designed to run efficiently on shared-memory, vector supercomputers.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 507 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. Lambers, James V.; Sumner, Amber C.: Explorations in numerical analysis (2019)
  5. Suman Rakshit; Adrian Baddeley; Gopalan Nair: Efficient Code for Second Order Analysis of Events on a Linear Network (2019) not zbMATH
  6. Bertaccini, Daniele; Durastante, Fabio: Iterative methods and preconditioning for large and sparse linear systems with applications (2018)
  7. Conte, S. D.; de Boor, Carl: Elementary numerical analysis. An algorithmic approach. Updated with MATLAB (2018)
  8. 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)
  9. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Randomized complete pivoting for solving symmetric indefinite linear systems (2018)
  10. Gustafsson, Bertil: Scientific computing. A historical perspective (2018)
  11. Alvarez, Amaury C.; García, Galina C.; Sarkis, Marcus: The ultra weak variational formulation for the modified mild-slope equation (2017)
  12. Anderson, Edward: Algorithm 978: Safe scaling in the level 1 BLAS (2017)
  13. Chen, Cheng; Fang, Jianbin; Tang, Tao; Yang, Canqun: LU factorization on heterogeneous systems: an energy-efficient approach towards high performance (2017)
  14. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  15. Echebest, N.; Schuverdt, M. L.; Vignau, R. P.: An inexact restoration derivative-free filter method for nonlinear programming (2017)
  16. Elmar Peise; Paolo Bientinesi: Algorithm 979: Recursive Algorithms for Dense Linear Algebra - The ReLAPACK Collection (2017) not zbMATH
  17. Gentle, James E.: Matrix algebra. Theory, computations and applications in statistics (2017)
  18. 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)
  19. Van Zee, Field G.; Smith, Tyler M.: Implementing high-performance complex matrix multiplication via the 3m and 4m methods (2017)
  20. Wang, Wei-Guo; Wei, Yimin: Mixed and componentwise condition numbers for matrix decompositions (2017)

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