Complexity Analysis of Interior Point Methods for Convex Quadratic Programming Based on a Parameterized Kernel Function

Abstract

abstract: The kernel functions play an important role in the amelioration of the computational complexity of algorithms. In this paper, we present a primal-dual interior-point algorithm for solving convex quadratic programming based on a new parametric kernel function. The proposed kernel function is not logarithmic and not self-regular. We analysis a large and small-update versions which are based on a new kernel function. We obtain the best known iteration bound for large-update methods, which improves significantly the so far obtained complexity results. This result is the first to reach this goal.