An Affine Scaling Trust-Region Algorithm for Solving the Nonlinear Equality Constrained Optimization

摘要

In this paper, the authors propose an affine scaling trust-region method in association with the nonmonotonic interior backtracking line search technique for solving the nonlinear equality constrained optimization subject to bounds on variables. By using both the trust-region strategy and the interior backtracking line search technique, each iterate switches to a backtracking step generated by the general trust-region subproblem and satisfies strict interior point feasibility by the line search backtracking technique. The global convergence and fast local convergence rate of the proposed algorithm are established under some reasonable conditions. Finally, some numerical esults are presented to illustrate the effectiveness of the proposed algorithm.