A Flexible Framework for Cubic Regularization Algorithms for Nonconvex Optimization in Function Space

被引：1
|
作者：
Schiela, Anton [1 ]
机构：
[1] Univ Bayreuth, Math Inst, D-95440 Bayreuth, Germany
来源：
NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION | 2019年 / 40卷 / 01期
关键词：
Non-convex optimization; optimization in function space; cubic regularization; GLOBAL CONVERGENCE; TRUST; MINIMIZATION;
D O I：
10.1080/01630563.2018.1499114
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We propose a cubic regularization algorithm that is constructed to deal with nonconvex minimization problems in function space. It allows for a flexible choice of the regularization term and thus accounts for the fact that in such problems one often has to deal with more than one norm. Global and local convergence results are established in a general framework.
引用
收藏
页码：85 / 118
页数：34
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