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 265 articles , 2 standard articles )

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  1. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  2. Cerdán, J.; Guerrero, D.; Marín, J.; Mas, J.: Preconditioners for rank deficient least squares problems (2020)
  3. Komala-Sheshachala, Sanjay; Sevilla, Ruben; Hassan, Oubay: A coupled HDG-FV scheme for the simulation of transient inviscid compressible flows (2020)
  4. Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: An overview of MINLP algorithms and their implementation in Muriqui optimizer (2020)
  5. Michel, Volker; Schneider, Naomi: A first approach to learning a best basis for gravitational field modelling (2020)
  6. Orban, Dominique; Siqueira, Abel Soares: A regularization method for constrained nonlinear least squares (2020)
  7. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  8. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  9. Andreani, Roberto; Ramirez, Viviana A.; Santos, Sandra A.; Secchin, Leonardo D.: Bilevel optimization with a multiobjective problem in the lower level (2019)
  10. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  11. Birgin, E. G.; Martínez, J. M.: A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization (2019)
  12. Hook, James; Pestana, Jennifer; Tisseur, Françoise; Hogg, Jonathan: Max-balanced Hungarian scalings (2019)
  13. Mejak, George: Closed form approximation of effective elastic moduli of composites with cubic, octet and cubic (+) octet periodic microstructures (2019)
  14. Paipuri, Mahendra; Tiago, Carlos; Fernández-Méndez, Sonia: Coupling of continuous and hybridizable discontinuous Galerkin methods: application to conjugate heat transfer problem (2019)
  15. Pandur, Marija Miloloža: Preconditioned gradient iterations for the eigenproblem of definite matrix pairs (2019)
  16. Scott, Jennifer A.; Tůma, Miroslav: Sparse stretching for solving sparse-dense linear least-squares problems (2019)
  17. D’ambra, Pasqua; Filippone, Salvatore; Vassilevski, Panayot S.: BootCMatch. A software package for bootstrap AMG based on graph weighted matching (2018)
  18. Druinsky, Alex; Carlebach, Eyal; Toledo, Sivan: Wilkinson’s inertia-revealing factorization and its application to sparse matrices. (2018)
  19. Dussault, Jean-Pierre: ARC(_q): a new adaptive regularization by cubics (2018)
  20. Gonzaga de Oliveira, Sanderson L.; Bernardes, Júnior A. B.; Chagas, Guilherme O.: An evaluation of low-cost heuristics for matrix bandwidth and profile reductions (2018)

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