A Large-Update Feasible Interior-Point Algorithm for Convex Quadratic Semi-definite Optimization Based on a New Kernel Function

Abstract

In this paper we present a large-update primal-dual interior-point algorithm for convex quadratic semi-definite optimization problems based on a new parametric kernel function. The goal of this paper is to investigate such a kernel function and show that the algorithm has the best complexity bound. The complexity bound is shown to be O(√ n log n log n).