A new parameterized logarithmic kernel function for linear optimization with a double barrier term yielding the best known iteration bound
In this paper, we propose a large-update primal-dual interior point algorithm for linear optimization. The method is based on a new class of kernel functions which differs from the existing kernel functions in which it has a double barrier term. The investigation according to it yields the best known iteration bound for large-update algorithm with the special choice of its parameter and thus improves the iteration bound obtained in Bai et al. [] for large-update algorithm.