Implementation ofan Interior-Point Algorithm Based on a Kernel Function for Weighted Linear Complementarity Problems

We consider the solution of weighted linear complementarity problems using interior-point algorithms, where the search directions are determined by a specific kernel function. To achieve a more efficient implementation, we modify the theoretically well-performing algorithm. The resulting methods are analyzed using a code written in the Python programming language. We present numerical results for problems whose solutions, based on previous theoretical findings, would require a very high number of iterations.