A class of primal-dual interior-point methods for convex quadratic optimization based on a parametric kernel function with a trigonometric barrier term

In this paper, a class of large- and small-update primal-dual interior-point methods for convex quadratic optimization based on a parametric kernel function with a trigonometric barrier term is proposed. By utilizing the feature of the parametric kernel function, we establish the worst case iteration bounds for both versions of the kernel-based interior-point methods, namely, ? ? 3 2 O n log(n /? ) andO? n log(n /? )? , respectively. These results match the ones obtained in the linear optimization case.