Several QP solvers for dense or sparse linear-quadratic optimization problems have been developed at the Engineering Mathematics Group. They are mainly used within SQP methods and for linear model-predictive control tasks. The following implementations are available:

• QPDO by Dr. Alberto De Marchi is designed for general convex QPs and is available at this link. It is a primal-dual Newton proximal method.
• Semi-smooth Newton method for sparse, large-scale, and strictly convex linear-quadratic problems (in Fortran 90)
• Semi-smooth Newton method for small and dense linear-quadratic problems (in C++)
• Primal-dual interior-point method for large-scale and sparse strictly convex linear-quadratic problems (in Fortran 90)
• Primal active set methods for small and dense and large-scale and sparse strictly convex linear-quadratic problems (in Fortran 90)
• Primal active-set method for small and dense strictly convex linear-quadratic problems (in C++)