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In this paper we present a generic primal-dualinterior point methods (IPMs) for linear optimization in which the search direction depends on a univariate kernel function which is also used as proximity measure in the analysis of the algorithm. The proposed kernel function does not satisfy all the conditions proposed in [2].We show that the corresponding large-update algorithm improves the iteration complexity with a factor $n^{\frac16}$ 
when compared with the method based on the use of the classical logarithmic barrier function. For small-update interior point methods the iteration bound is $O(\sqrt{n}\log\frac{n}{\epsilon}),$ 
which is currently the best-known bound for primal-dual IPMs.
