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

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  1. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  2. Exler, Oliver; Lehmann, Thomas; Schittkowski, Klaus: A comparative study of SQP-type algorithms for nonlinear and nonconvex mixed-integer optimization (2012)
  3. Schittkowski, K.: A robust implementation of a sequential quadratic programming algorithm with successive error restoration (2011)
  4. Andretta, Marina; Birgin, Ernesto G.; Martínez, J.M.: Partial spectral projected gradient method with active-set strategy for linearly constrained optimization (2010)
  5. Schittkowski, Klaus: An active set strategy for solving optimization problems with up to 200,000,000 nonlinear constraints (2009)
  6. Dai, Yu-Hong; Schittkowski, Klaus: A sequential quadratic programming algorithm with non-monotone line search (2008)
  7. Huebner, E.; Tichatschke, R.: Relaxed proximal point algorithms for variational inequalities with multi-valued operators (2008)
  8. Liska, Richard; Shashkov, Mikhail: Enforcing the discrete maximum principle for linear finite element solutions of second-order elliptic problems (2008)
  9. Exler, Oliver; Schittkowski, Klaus: A trust region SQP algorithm for mixed-integer nonlinear programming (2007)
  10. Zanni, Luca: An improved gradient projection-based decomposition technique for support vector machines (2006)
  11. Schittkowski, K.: Optimal parameter selection in support vector machines (2005)