Abstract
An interior-point trust-region algorithm is proposed for minimizing a general (non-convex) quadratic objective function in the intersection of a symmetric cone and an affine subspace. The algorithm uses a trust-region model to ensure descent on a suitable merit function. Global first-order and second-order convergence results are proved. Numerical results are presented.
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