An interior-point algorithm for convex quadratic programming over symmetric cones

This paper analyses an interior-point algorithm for solving convex quadratic programming over symmetric cones. Here the objective function is a convex quadratic function and the feasible set is the intersection of an affine subspace and a symmetric cone associated with a Euclidean Jordan algebra. The algorithm utilizes the large class of commutative search directions introduced by Schmieta and Alizadeh [10] for arbitrary symmetric cones. The algorithm follows a wide neighborhood of the central path which makes the iterates capable of moving towards optimality with longer steps. Despite passing this pleasant feature, the complexity iteration bound still remains as good as the same result of Schmieta and Alizadeh for symmetric cone optimization problems.