In this study, we propose a trust-region-based procedure to solve unco-strained optimization problems that take advantage of the nonmonotone technique to introduce an ecient adaptive radius strategy. In our approach, the adaptive tech- nique leads to decreasing the total number of iterations, while utilizing the structure of nonmonotone formula helps us to handle large-scale problems. The new algorithm preserves the global convergence and has quadratic convergence under suitable con- ditions. Preliminary numerical experiments on standard test problems indicate the eciency and robustness of the proposed approach for solving unconstrained opti- mization problems.
