A Non-Monotone Trust Region Algorithm with Memory Model for Unconstrained Optimization

摘要

In this study, we developed a non-monotone trust region algorithm for unconstrained optimization. Different from the tradition non-monotone trust region algorithm, this algorithm includes memory model which make the algorithm more farsighted in the sense that its behavior is not completely dominated by the local nature of the objective function. We presented a non-monotone trust region algorithm that has this feature and prove its global convergence under suitable conditions.