  A constraint-reduced Mehrotra-Predictor-Corrector algorithm for convex
quadratic programming is proposed. (At each iteration, such algorithms use only
a subset of the inequality constraints in constructing the search direction,
resulting in CPU savings.) The proposed algorithm makes use of a regularization
scheme to cater to cases where the reduced constraint matrix is rank deficient.
Global and local convergence properties are established under arbitrary
working-set selection rules subject to satisfaction of a general condition. A
modified active-set identification scheme that fulfills this condition is
introduced. Numerical tests show great promise for the proposed algorithm, in
particular for its active-set identification scheme. While the focus of the
present paper is on dense systems, application of the main ideas to large
sparse systems is briefly discussed.
