Convergence of Affine-Scaling Interior-Point Methods with Line Search for Box Constrained Optimization

Abstract

A class of affine-scaling interior-point trust region methods with line search for bound constrained optimization problems is presented in this article. The method is globally convergent under the standard assumptions and is locally q-superlinear or q-quadratic convergent in the case where the strong second order sufficient optimality conditions at the solution are satisfied, but the strict complementarity condition is violated. Numerical experiments show that the method is efficient.