A new regularized quasi-Newton method for unconstrained optimization
点击次数：
所属单位：理学院
发表刊物：OPTIMIZATION LETTERS
关键字：Unconstrained optimization Regularized quasi-Newton method Non-monotone line search
摘要：In this paper, we propose a new regularized quasi-Newton method for unconstrained optimization. At each iteration, a regularized quasi-Newton equation is solved to obtain the search direction. The step size is determined by a non-monotone Armijo backtracking line search. An adaptive regularized parameter, which is updated according to the step size of the line search, is employed to compute the next search direction. The presented method is proved to be globally convergent. Numerical experiments show that the proposed method is effective for unconstrained optimizations and outperforms the existing regularized Newton method.
ISSN号：1862-4472
是否译文：否
发表时间：2018-10-01
合写作者：张浩,张浩
通讯作者：张浩,倪勤,张浩