Hybrid CGME and TCGME algorithms for large-scale general-form regularization
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866917762944729088 |
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| author | Yang, Yanfei |
| author_facet | Yang, Yanfei |
| contents | Two new hybrid algorithms are proposed for large-scale linear discrete ill-posed problems in general-form regularization. They are both based on Krylov subspace inner-outer iterative algorithms. At each iteration, they need to solve a linear least squares problem, which is the inner least squares problem. It is proved that inner linear least squares problems, solved by LSQR, become better conditioned as k increases, so LSQR converges faster. We also prove how to choose the stopping tolerance for LSQR to guarantee that the computed and exact best regularized solutions have the same accuracy. Numerical experiments are provided to demonstrate the effectiveness and efficiency of our new hybrid algorithms, along with comparisons to the existing algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_04078 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hybrid CGME and TCGME algorithms for large-scale general-form regularization Yang, Yanfei Numerical Analysis 65F22, 65F10, 65J20, 65F35, 65F50 Two new hybrid algorithms are proposed for large-scale linear discrete ill-posed problems in general-form regularization. They are both based on Krylov subspace inner-outer iterative algorithms. At each iteration, they need to solve a linear least squares problem, which is the inner least squares problem. It is proved that inner linear least squares problems, solved by LSQR, become better conditioned as k increases, so LSQR converges faster. We also prove how to choose the stopping tolerance for LSQR to guarantee that the computed and exact best regularized solutions have the same accuracy. Numerical experiments are provided to demonstrate the effectiveness and efficiency of our new hybrid algorithms, along with comparisons to the existing algorithm. |
| title | Hybrid CGME and TCGME algorithms for large-scale general-form regularization |
| topic | Numerical Analysis 65F22, 65F10, 65J20, 65F35, 65F50 |
| url | https://arxiv.org/abs/2301.04078 |