NR-SSOR right preconditioned RRGMRES for arbitrary singular systems and least squares problems

Abstract: 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.