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.
This software is also referenced in ORMS.
Keywords for this software
References in zbMATH (referenced in 214 articles , 2 standard articles )
Showing results 1 to 20 of 214.
Sorted by year (- Dussault, Jean-Pierre: ARC$_q$: a new adaptive regularization by cubics (2018)
- 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)
- Hook, James; Scott, Jennifer; Tisseur, Françoise; Hogg, Jonathan: A Max-plus approach to incomplete Cholesky factorization preconditioners (2018)
- Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
- Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: Integrality gap minimization heuristics for binary mixed integer nonlinear programming (2018)
- Muldoon, F. H.: Numerical study of hydrothermal wave suppression in thermocapillary flow using a predictive control method (2018)
- Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
- Gould, Nicholas; Scott, Jennifer: The state-of-the-art of preconditioners for sparse linear least-squares problems (2017)
- Greif, Chen; Rees, Tyrone; Szyld, Daniel B.: GMRES with multiple preconditioners (2017)
- Hall, Edward; Houston, Paul; Murphy, Steven: $hp$-adaptive discontinuous Galerkin methods for neutron transport criticality problems (2017)
- Hijazi, Hassan; Coffrin, Carleton; Van Hentenryck, Pascal: Convex quadratic relaxations for mixed-integer nonlinear programs in power systems (2017)
- Jaensson, N. O.; Hulsen, M. A.; Anderson, P. D.: A comparison between the XFEM and a boundary-fitted mesh method for the simulation of rigid particles in Cahn-Hilliard fluids (2017)
- Koppenol, Daniël C.; Vermolen, Fred J.; Koppenol-Gonzalez, Gabriela V.; Niessen, Frank B.; van Zuijlen, Paul P. M.; Vuik, Kees: A mathematical model for the simulation of the contraction of burns (2017)
- Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
- Machat, Hela; Carrayrou, Jér^ome: Comparison of linear solvers for equilibrium geochemistry computations (2017)
- Orban, Dominique; Arioli, Mario: Iterative solution of symmetric quasi-definite linear systems (2017)
- Pichon, Gregoire; Faverge, Mathieu; Ramet, Pierre; Roman, Jean: Reordering strategy for blocking optimization in sparse linear solvers (2017)
- Quirynen, Rien; Gros, Sébastien; Houska, Boris; Diehl, Moritz: Lifted collocation integrators for direct optimal control in ACADO toolkit (2017)
- Scott, Jennifer; Tuma, Miroslav: Solving mixed sparse-dense linear least-squares problems by preconditioned iterative methods (2017)
- Andreani, R.; Júdice, J. J.; Martínez, J. M.; Martini, T.: Feasibility problems with complementarity constraints (2016)