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The Regularized Global GMERR Method for Solving Large-Scale Linear Discrete Ill-Posed Problems
Hui Zhang and Hua Dai

East Asian J. Appl. Math. DOI: 10.4208/eajam.2023-161.081023

Publication Date : 2024-05-20

  • Abstract

For the large-scale linear discrete ill-posed problems with multiple right-hand sides, the global Krylov subspace iterative methods have received a lot of attention. In this paper, we analyze the regularizing properties of the global generalized minimum error method (GMERR), and develop a regularized global GMERR method for solving linear discrete ill-posed problems with multiple right-hand sides. The efficiency of the proposed method is confirmed by the numerical experiments on test matrices.

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