A new nonmonotone adaptive trust region line search method for unconstrained optimization

This paper proposes a new nonmonotone adaptive trust region line search method for solving unconstrained optimization problems, and presents a modified trust region ratio, which obtained more reasonable consistency between the accurate model and the approximate model. The approximation of Hessian matrix is updated by the modified BFGS formula. Trust region radius adopts a new adaptive strategy to overcome additional computational costs at each iteration. The global convergence and superlinear convergence of the method are preserved under suitable conditions. Finally, the numerical results show that the proposed method is very efficient.