In this paper we present an infeasible primal-dual interior-point algorithm for linearly constrained convex optimization based on a parametric kernel function, with parameters $p\in[0,1]$ and $q \geq 1$. Numerical test shows that the efficiency of the proposed algorithm and investigates the behavior of the algorithm with different parameters $p,q$ and $\theta$.
['Guoqiang Wang', 'Baocun Wang', 'Qingduan Fan']
An Infeasible Primal-Dual Interior-Point Algorithm for Linearly Constrained Convex Optimization Based on a Parametric Kernel Function