A Fast Data-Driven Iteratively Regularized Method with Convex Penalty for Solving Ill-Posed Problems-Reference-Cited by-同舟云学术

A Fast Data-Driven Iteratively Regularized Method with Convex Penalty for Solving Ill-Posed Problems

Published:2023-05-12 Issue:2 Volume:16 Page:640-670
ISSN:1936-4954
Container-title:SIAM Journal on Imaging Sciences
language:en
Short-container-title:SIAM J. Imaging Sci.

Author:

Gao Guangyu¹^ORCID,Han Bo¹,Fu Zhenwu²^ORCID,Tong Shanshan³

Affiliation:

1. Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang Province, 150001, People’s Republic of China.

2. Corresponding Author. Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang Province, 150001, People’s Republic of China.

3. Corresponding Author. School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, Shaanxi Province, 710119, People’s Republic of China.

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,General Mathematics

Reference39 articles.

1. Regularization of Inverse Problems

2. Iterative Regularization Methods for Nonlinear Ill-Posed Problems

3. Inverse Problems

4. A convergence analysis of the Landweber iteration for nonlinear ill-posed problems

5. The Averaged Kaczmarz Iteration for Solving Inverse Problems

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