We propose a feasible active set method for convex quadratic programming prob-\udlems with nonnegativity constraints. This method is specifically designed to be embedded into\uda branch-and-bound algorithm for convex quadratic mixed- integer programming problems. The\udbranch-and-bound algorithm generalizes the approach for unconstrained convex quadratic integer\udprogramming proposed by Buchheim, Caprara, and Lodi [ Math. Program., 135 (2012), pp. 369–\ud395] to the presence of linear constraints. The main feature of the latter approach consists of a\udsophisticated preprocessing phase, leading to a fast enumeration of the branch-and-bound nodes.\udMoreover, the feasible active set method takes advantage of this preprocessing phase and is well\udsuited for reoptimization. Experimental results for randomly generated instances show that the new\udapproach significantly outperforms the MIQP solver of CPLEX 12.6 for instances with a small number\udof constraints.
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