Ill-posed problems and the conjugate gradient method: Optimal convergence rates in the presence of discretization and modelling errors

被引：1

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 | 2022年 / 30卷 / 06期

关键词：

Ill-posed problems; conjugate gradient; discrepancy principle; REGULARIZATION;

D O I：

10.1515/jiip-2022-0039

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we prove order-optimal convergence rates for the conjugate gradient method applied to linear ill-posed problems when not only the data are noisy but also when the operator is perturbed via discretization and modelling errors.

引用

页码：905 / 915

页数：11

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