Primal-dual interior-point methods solver based on kernel functions for Linear Optimization

In this paper we present the theory and practical aspects of implementing the path following interior point methods for linear optimization, based on kernel functions. We will investigate the influence of the choice of the kernel function on the computational behavior of the generic primal-dual algorithm for Linear Optimization. We find that the finite kernel function gives the best results for more than 50 % of the tested problems compared to the standard log-barrier method