摘要
In this paper, we present a regularized Newton method (M-RNM) with correction for minimizing a convex function whose Hessian matrices may be singular. At every iteration, not only a RNM step is computed but also two correction steps are computed. We show that if the objective function is LC<sup>2</sup>, then the method posses globally convergent. Numerical results show that the new algorithm performs very well.
In this paper, we present a regularized Newton method (M-RNM) with correction for minimizing a convex function whose Hessian matrices may be singular. At every iteration, not only a RNM step is computed but also two correction steps are computed. We show that if the objective function is LC<sup>2</sup>, then the method posses globally convergent. Numerical results show that the new algorithm performs very well.
作者
Liming Li
Mei Qin
Heng Wang
Liming Li;Mei Qin;Heng Wang(College of Science, University of Shanghai for Science and Technology, Shanghai, China)
