A New Nonmonotone Adaptive Trust Region Method for Unconstrained Optimization

In this paper, we propose a nonmonotone adaptive trust region method for unconstrained optimization. Unlike traditional nonmonotone trust region methods, we construct a new ratio of actual descent and predicted descent and at each iteration this method can automatically adjust the trust region radius of related sub problem. The global convergence result of the algorithm is established.