A trust region algorithm with adaptive cubic regularization methods for nonsmooth convex minimization

被引：0
|
作者：
Sha Lu
Zengxin Wei
Lue Li
机构：
[1] Guangxi University,School of Mathematics and Information Science
[2] Guangxi Teachers Education University,School of Mathematical Science
[3] Guangxi Normal University,School of Mathematical Sciences
来源：
Computational Optimization and Applications | 2012年 / 51卷
关键词：
Trust region method; Nonsmooth convex minimization; Moreau-Yosida regularization; Proximal method; Cubic overestimation model;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
By using the Moreau-Yosida regularization and proximal method, a new trust region algorithm is proposed for nonsmooth convex minimization. A cubic subproblem with adaptive parameter is solved at each iteration. The global convergence and Q-superlinear convergence are established under some suitable conditions. The overall iteration bound of the proposed algorithm is discussed. Preliminary numerical experience is reported.
引用
收藏
页码：551 / 573
页数：22
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