An interior-point trust-region polynomial algorithm for convex quadratic minimization subject to general convex constraints.

An interior-point trust-region algorithm is proposed for minimization of a convex quadratic objective function over a general convex set. The algorithm uses a trust-region model to ensure descent on a suitable merit function. The complexity of our algorithm is proved to be as good as the interior-point polynomial algorithm. [ABSTRACT FROM AUTHOR]