Formulating and Solving Nonlinear Programs as Mixed Complementarity Problems

Abstract

We consider a primal-dual approach to solve nonlinear programming problems within AMPL modeling language, via a mixed complementarity formulation. The modeling language supplies the first order and second order derivative information of the Lagrangian function of the nonlinear problem using automatic differentiation. The PATH solver finds the solution of the first order conditions which are generated automatically from this derivative information. In addition, the link incorporates objective function into a new merit function for the PATH solver to improve the capability of the complementarity algorithm for finding optimal solutions of the nonlinear program. We test the new solver on various test suits from the literature and compare with other available nonlinear programming solvers.