Complexity analysis and numerical implementation of large-update interior-point methods for SDLCP based on a new parametric barrier kernel function

In this paper, we deal with the complexity analysis and the numerical implementation of primal-dual interior-point methods for monotone semidefinite linear complementarity problems based on a new parametric kernel function. The proposed kernel function is neither a self-regular and nor the usual logarithmic barrier function. By means of the feature of the parametric kernel function, we study the complexity analysis of primal-dual IPMs and derive the currently best known iteration bound for the large-update algorithm, namely, which is as good as the linear and the semidefinite optimization analogue. Finally, we report some numerical results to show the practical performance of the proposed algorithm with different parameters.