An affine-scaling interior-point filter line-search algorithm for constrained optimization

Abstract

This paper presents and analyzes an affine-scaling interior-point algorithm with a filter line-search method for solving nonlinear optimization problems with nonlinear equality constraints and nonnegative variables. In our scheme, we require that a damped Newton’s method is applied to the perturbed first-order necessary conditions to produce a search direction. Some filtered rules for a fixed barrier parameter are used to determine step acceptance. Second-order correction technique is used to reduce infeasibility and overcome the Maratos effect. The global convergence and fast local convergence rate of the proposed algorithm are established under some suitable conditions.