A full-Newton step interior-point algorithm for linear optimization based on a finite barrier

In this paper, we propose a finite barrier kernel function for primal-dual interior-point algorithm in linear optimization with a full-Newton step. To our best knowledge, it is the first time that the property of exponential convexity is used for full-Newton step interior-point methods(IPMs). Moreover, the analysis is simplified and the complexity of the algorithm coincides with the currently best iteration bound for linear optimization problems.