期刊
INVERSE PROBLEMS
卷 34, 期 3, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/1361-6420/aaa0fb
关键词
nonlinear inverse problems; the iteratively regularized Gauss-Newton method; heuristic selection rule; a posteriori error estimates; convergence
资金
- Future Fellowship of the Australian Research Council
- National Natural Science Foundation of China [11401257]
The iteratively regularized Gauss-Newton method is one of the most prominent regularization methods for solving nonlinear ill-posed inverse problems when the data is corrupted by noise. In order to produce a useful approximate solution, this iterative method should be terminated properly. The existing a priori and a posteriori stopping rules require accurate information on the noise level, which may not be available or reliable in practical applications. In this paper we propose a heuristic selection rule for this regularization method, which requires no information on the noise level. By imposing certain conditions on the noise, we derive a posteriori error estimates on the approximate solutions under various source conditions. Furthermore, we establish a convergence result without using any source condition. Numerical results are presented to illustrate the performance of our heuristic selection rule.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据