A Trust-Region Algorithm for Nonlinear Inequality Constrained Optimization

Abstract

This paper presents a new trust-region algorithm for $n$-dimension nonlinear optimization subject to $m$ nonlinear inequality constraints. Equivalent KKT conditions are derived, which is the basis for constructing the new algorithm. Global convergence of the trial steps, local quadratic convergence theorem is proved for nondegenerate minimizer point. Numerical experiment is presented to show the effectiveness of our approach.