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

Jia Gu, Shuang Wang, Jinwei Yang

Abstract


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.

Keywords


Interior-point methods; Convex quadratic optimization; Large- and small-update methods; Polynomial complexity.

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