An Improved Trust Region Method for Unconstrained Optimization

In this paper, we propose a new improved nonmonotone adaptive trust region method for solving the unconstrained optimization problems. Trust region methods are a class of effective iterative schemes in Optimization. In this framework, we use an approximate model to calculate Hessian matrix. So, the Hessian matrix is updated by the scaled memoryless BFGS formula. Furthermore, the trust region rdius is updated by an efficient rule. Under some suitable and standard assumptions, we proof the global convergence properties of the new algorithm.

کلیدواژه ها:

Trust region method ، Adaptive radius ، Hessian matrix ، Global convergence