NR-SSOR right preconditioned RRGMRES for arbitrary singular systems and least squares problems
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| Format: | Preprint |
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2025
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| author | Sugihara, Kouta Hayami, Ken |
| author_facet | Sugihara, Kouta Hayami, Ken |
| contents | GMRES is known to determine a least squares solution of $ A x = b $ where $ A \in R^{n \times n} $ without breakdown for arbitrary $ b \in R^n $, and initial iterate $ x_0 \in R^n $ if and only if $ A $ is range-symmetric, i.e. $ R(A^T) = R(A) $, where $ A $ may be singular and $ b $ may not be in the range space $ R(A) $ of $ A $.
In this paper, we propose applying the Range Restricted GMRES (RRGMRES) to $ A C A^T z = b $, where $ C \in R^{n \times n} $ is symmetric positive definite. This determines a least squares solution $ x = C A^T z $ of $ A x = b $ without breakdown for arbitrary (singular) matrix $ A \in R^{n \times n} $ and $ b, x_0 \in R^n $, and is much more stable and accurate compared to GMRES, RRGMRES and MINRES-QLP applied to $ A x = b $ for inconsistent problems when $ b \notin R(A) $. In particular, we propose applying the NR-SSOR as the inner iteration right preconditioner, which also works efficiently for least squares problems $ \min_{x \in R^n} \| b - A x\|_2 $ for $ A \in R^{m \times n} $ and arbitrary $ b \in R^m $.
Numerical experiments demonstrate the validity of the proposed method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_09891 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NR-SSOR right preconditioned RRGMRES for arbitrary singular systems and least squares problems Sugihara, Kouta Hayami, Ken Numerical Analysis 65F10, 65F08, 65F20, 65F50 GMRES is known to determine a least squares solution of $ A x = b $ where $ A \in R^{n \times n} $ without breakdown for arbitrary $ b \in R^n $, and initial iterate $ x_0 \in R^n $ if and only if $ A $ is range-symmetric, i.e. $ R(A^T) = R(A) $, where $ A $ may be singular and $ b $ may not be in the range space $ R(A) $ of $ A $. In this paper, we propose applying the Range Restricted GMRES (RRGMRES) to $ A C A^T z = b $, where $ C \in R^{n \times n} $ is symmetric positive definite. This determines a least squares solution $ x = C A^T z $ of $ A x = b $ without breakdown for arbitrary (singular) matrix $ A \in R^{n \times n} $ and $ b, x_0 \in R^n $, and is much more stable and accurate compared to GMRES, RRGMRES and MINRES-QLP applied to $ A x = b $ for inconsistent problems when $ b \notin R(A) $. In particular, we propose applying the NR-SSOR as the inner iteration right preconditioner, which also works efficiently for least squares problems $ \min_{x \in R^n} \| b - A x\|_2 $ for $ A \in R^{m \times n} $ and arbitrary $ b \in R^m $. Numerical experiments demonstrate the validity of the proposed method. |
| title | NR-SSOR right preconditioned RRGMRES for arbitrary singular systems and least squares problems |
| topic | Numerical Analysis 65F10, 65F08, 65F20, 65F50 |
| url | https://arxiv.org/abs/2504.09891 |