An improved trust region method for unconstrained optimization

摘要

In this paper, a new trust region method for unconstrained optimization is proposed. In the new method, the trust radius adjusts itself adaptively. In our algorithm, we use the convex combination of the Hessian matrix at a previous iteration and current iteration to define a suitable trust region radius at each iteration. The global, superlinear and quadratic convergence results of the algorithm are established under reasonable assumptions. Finally, some numerical results are given.