QL: A Fortran Code for Convex Quadratic Programming. The Fortran subroutine QL solves strictly convex quadratic programming problems subject to linear equality and inequality constraints by the primal-dual method of Goldfarb and Idnani. An available Cholesky decomposition of the objective function matrix can be provided by the user. Bounds are handled separately. The code is designed for solving small-scale quadratic programs in a numerically stable way. Its usage is outlined and an illustrative example is presented

References in zbMATH (referenced in 16 articles )

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  1. Klima, Matej; Barlow, Andrew; Kucharik, Milan; Shashkov, Mikhail: An interface-aware sub-scale dynamics multi-material cell model for solids with void closure and opening at all speeds (2020)
  2. Gould, Nicholas I. M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  3. Lutsyshyn, Y.: Fast quantum Monte Carlo on a GPU (2015)
  4. Barlow, Andrew; Hill, Ryan; Shashkov, Mikhail: Constrained optimization framework for interface-aware sub-scale dynamics closure model for multimaterial cells in Lagrangian and arbitrary Lagrangian-Eulerian hydrodynamics (2014)
  5. Exler, Oliver; Lehmann, Thomas; Schittkowski, Klaus: A comparative study of SQP-type algorithms for nonlinear and nonconvex mixed-integer optimization (2012)
  6. Schittkowski, K.: A robust implementation of a sequential quadratic programming algorithm with successive error restoration (2011)
  7. Andretta, Marina; Birgin, Ernesto G.; Martínez, J. M.: Partial spectral projected gradient method with active-set strategy for linearly constrained optimization (2010)
  8. Schittkowski, Klaus: An active set strategy for solving optimization problems with up to 200,000,000 nonlinear constraints (2009)
  9. Dai, Yu-Hong; Schittkowski, Klaus: A sequential quadratic programming algorithm with non-monotone line search (2008)
  10. Huebner, E.; Tichatschke, R.: Relaxed proximal point algorithms for variational inequalities with multi-valued operators (2008)
  11. Liska, Richard; Shashkov, Mikhail: Enforcing the discrete maximum principle for linear finite element solutions of second-order elliptic problems (2008)
  12. Exler, Oliver; Schittkowski, Klaus: A trust region SQP algorithm for mixed-integer nonlinear programming (2007)
  13. Zanni, Luca: An improved gradient projection-based decomposition technique for support vector machines (2006)
  14. Schittkowski, K.: Optimal parameter selection in support vector machines (2005)
  15. Young, Martin R.; DeSarbo, Wayne S.: A parametric procedure for ultrametric tree estimation from conditional rank order proximity data (1995)
  16. Schittkowski, Klaus: EMP: An expert system for mathematical programming (1988)