In this article, we extend kernel-based interior point algorithms for linear programming to convex quadratic programming overs second-order cones. By means of Jordan algebras, we establish the iteration complexity for long- and short-step interior-point methods, namely, equation and equation, respectively. These results coincide with the ones obtained in the linear programming case.
['Yanfang Li', 'Xibo Duan', 'Jia Gu']
On extending kernel-based interior point algorithms for linear programming to convex quadratic second-order cone programming