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 275 articles , 2 standard articles )

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  1. Dandurand, Brian C.; Kim, Kibaek; Leyffer, Sven: A bilevel approach for identifying the worst contingencies for nonconvex alternating current power systems (2021)
  2. Kanzow, Christian; Raharja, Andreas B.; Schwartz, Alexandra: An augmented Lagrangian method for cardinality-constrained optimization problems (2021)
  3. Manguoğlu, Murat; Volker, Mehrmann: A two-level iterative scheme for general sparse linear systems based on approximate skew-symmetrizers (2021)
  4. Robuschi, Nicolò; Zeile, Clemens; Sager, Sebastian; Braghin, Francesco: Multiphase mixed-integer nonlinear optimal control of hybrid electric vehicles (2021)
  5. Wambacq, J.; Ulloa, J.; Lombaert, G.; François, S.: Interior-point methods for the phase-field approach to brittle and ductile fracture (2021)
  6. Al-Baali, Mehiddin; Caliciotti, Andrea; Fasano, Giovanni; Roma, Massimo: A class of approximate inverse preconditioners based on Krylov-subspace methods for large-scale nonconvex optimization (2020)
  7. Birgin, E. G.; Martínez, J. M.: Complexity and performance of an augmented Lagrangian algorithm (2020)
  8. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  9. Caliciotti, Andrea; Fasano, Giovanni; Potra, Florian; Roma, Massimo: Issues on the use of a modified bunch and Kaufman decomposition for large scale Newton’s equation (2020)
  10. Cerdán, J.; Guerrero, D.; Marín, J.; Mas, J.: Preconditioners for rank deficient least squares problems (2020)
  11. De Leone, Renato; Fasano, Giovanni; Roma, Massimo; Sergeyev, Yaroslav D.: Iterative grossone-based computation of negative curvature directions in large-scale optimization (2020)
  12. Komala-Sheshachala, Sanjay; Sevilla, Ruben; Hassan, Oubay: A coupled HDG-FV scheme for the simulation of transient inviscid compressible flows (2020)
  13. Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: An overview of MINLP algorithms and their implementation in Muriqui optimizer (2020)
  14. Michel, Volker; Schneider, Naomi: A first approach to learning a best basis for gravitational field modelling (2020)
  15. Orban, Dominique; Siqueira, Abel Soares: A regularization method for constrained nonlinear least squares (2020)
  16. Paul F. Lang, Sungho Shin, Victor M. Zavala: SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization (2020) arXiv
  17. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  18. Alrehaili, A. H.; Walkley, M. A.; Jimack, P. K.; Hubbard, Matthew E.: An efficient numerical algorithm for a multiphase tumour model (2019)
  19. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  20. Andreani, Roberto; Ramirez, Viviana A.; Santos, Sandra A.; Secchin, Leonardo D.: Bilevel optimization with a multiobjective problem in the lower level (2019)

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