  We propose a feasible active set method for convex quadratic programming
problems with non-negativity constraints. This method is specifically designed
to be embedded into a branch-and-bound algorithm for convex quadratic mixed
integer programming problems. The branch-and-bound algorithm generalizes the
approach for unconstrained convex quadratic integer programming proposed by
Buchheim, Caprara and Lodi to the presence of linear constraints. The main
feature of the latter approach consists in a sophisticated preprocessing phase,
leading to a fast enumeration of the branch-and-bound nodes. Moreover, the
feasible active set method takes advantage of this preprocessing phase and is
well suited for reoptimization. Experimental results for randomly generated
instances show that the new approach significantly outperforms the MIQP solver
of CPLEX 12.6 for instances with a small number of constraints.
