文章基本信息

标题：A Regularized Newton Method with Correction for Unconstrained Convex Optimization
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作者：Liming Li ; Mei Qin ; Heng Wang 等
期刊名称：Open Journal of Optimization
印刷版ISSN：2325-7105
电子版ISSN：2325-7091
出版年度：2016
卷号：05
期号：01
页码：44-52
DOI：10.4236/ojop.2016.51006
语种：English
出版社：Scientific Research Publishing
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 LC2, then the method posses globally convergent. Numerical results show that the new algorithm performs very well.
关键词：Regularied Newton Method;Correction Technique;Trust Region Technique;Unconstrained Convex Optimization