HSL

HSL (formerly the Harwell Subroutine Library) is a collection of state-of-the-art packages for large-scale scientific computation written and developed by the Numerical Analysis Group at the STFC Rutherford Appleton Laboratory and other experts. HSL offers users a high standard of reliability and has an international reputation as a source of robust and efficient numerical software. Among its best known packages are those for the solution of sparse linear systems of equations and sparse eigenvalue problems. MATLAB interfaces are offered for selected packages. The Library was started in 1963 and was originally used at the Harwell Laboratory on IBM mainframes running under OS and MVS. Over the years, the Library has evolved and has been extensively used on a wide range of computers, from supercomputers to modern PCs. Recent additions include optimised support for multicore processors. If you are interested in our optimization or nonlinear equation solving packages, our work in this area is released in the GALAHAD library.

This software is also referenced in ORMS.


References in zbMATH (referenced in 145 articles , 2 standard articles )

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  1. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  2. Hogg, Jonathan D.; Ovtchinnikov, Evgueni; Scott, Jennifer A.: A sparse symmetric indefinite direct solver for GPU architectures (2016)
  3. Paszyński, Maciej: Fast solvers for mesh-based computations (2016)
  4. Scott, Jennifer; Tuma, Miroslav: Preconditioning of linear least squares by robust incomplete factorization for implicitly held normal equations (2016)
  5. Arioli, Mario; Duff, Iain S.: Preconditioning linear least-squares problems by identifying a basis matrix (2015)
  6. Burzyński, Stanisław; Chróścielewski, Jacek; Witkowski, Wojciech: Elastoplastic law of Cosserat type in shell theory with drilling rotation (2015)
  7. Gill, Philip E.; Wong, Elizabeth: Methods for convex and general quadratic programming (2015)
  8. Gould, Nick I.M.; Orban, Dominique; Toint, Philippe L.: An interior-point $\ell_1$-penalty method for nonlinear optimization (2015)
  9. Koch, Thorsten (ed.); Hiller, Benjamin (ed.); Pfetsch, Marc (ed.); Schewe, Lars (ed.): Evaluating gas network capacities (2015)
  10. Orban, Dominique: Limited-memory LDL$^\top$ factorization of symmetric quasi-definite matrices with application to constrained optimization (2015)
  11. Hogg, J.D.; Scott, J.A.: Compressed threshold pivoting for sparse symmetric indefinite systems (2014)
  12. Scott, Jennifer; Tůma, Miroslav: HSL_MI28: an efficient and robust limited-memory incomplete Cholesky factorization code (2014)
  13. Wu, Jian-ping; Song, Jun-qiang; Zhang, Wei-min: An efficient and accurate method to compute the Fiedler vector based on Householder deflation and inverse power iteration (2014)
  14. Benner, Peter; Saak, Jens; Stoll, Martin; Weichelt, Heiko K.: Efficient solution of large-scale saddle point systems arising in Riccati-based boundary feedback stabilization of incompressible Stokes flow (2013)
  15. Canedo Alonso, M.M.; González-Hernández, J.L.; Encinar Del Dedo, S.: Application the computational method KINMODEL(AGDC) to the simultaneous determination of kinetic and analytical parameters (2013)
  16. Donatelli, M.; Semplice, M.; Serra-Capizzano, S.: AMG preconditioning for nonlinear degenerate parabolic equations on nonuniform grids with application to monument degradation (2013)
  17. Gould, Nicholas I.M.; Orban, Dominique; Robinson, Daniel P.: Trajectory-following methods for large-scale degenerate convex quadratic programming (2013)
  18. Hogg, J.D.; Scott, J.A.: Optimal weighted matchings for rank-deficient sparse matrices (2013)
  19. Hogg, Jonathan D.; Scott, Jennifer A.: Pivoting strategies for tough sparse indefinite systems (2013)
  20. Skanda, Dominik; Lebiedz, Dirk: A robust optimization approach to experimental design for model discrimination of dynamical systems (2013)

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