Optimal convergence rates for inexact Newton regularization with CG as inner iteration

被引：5
|
作者：
Neubauer, Andreas [1 ]
机构：
[1] Johannes Kepler Univ Linz, Ind Math Inst, A-4040 Linz, Austria
来源：
JOURNAL OF INVERSE AND ILL-POSED PROBLEMS | 2020年 / 28卷 / 01期
关键词：
Nonlinear ill-posed problems; inexact Newton iteration; conjugate gradient; discrepancy principle;
D O I：
10.1515/jiip-2019-0092
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper we prove order optimality of an inexact Newton regularization method, where the linearized equations are solved approximately using the conjugate gradient method. The outer and inner iterations are stopped via the discrepancy principle. We show that the conditions needed for convergence rates are satisfied for a certain parameter identification problem.
引用
收藏
页码：145 / 153
页数：9
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