A stabilized GMRES method for singular and severely ill-conditioned systems of linear equations

被引:1
|
作者
Liao, Zeyu [1 ]
Hayami, Ken [2 ]
Morikuni, Keiichi [3 ]
Yin, Jun-Feng [4 ]
机构
[1] Grad Univ Adv Studies SOKENDAI, Sch Multidisciplinary Sci, Dept Informat, Chiyoda Ku, 2-1-2 Hitotsubashi, Tokyo 1018430, Japan
[2] Grad Univ Adv Studies SOKENDAI, Natl Inst Informat, Chiyoda Ku, 2-1-2 Hitotsubashi, Tokyo 1018430, Japan
[3] Univ Tsukuba, Fac Engn Informat & Syst, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan
[4] Tongji Univ, Sch Math Sci, Siping Rd 1239, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
Least squares problems; Krylov subspace methods; GMRES; Inconsistent systems; Minimum-norm solution; Regularization; KRYLOV SUBSPACE METHODS; LEAST-SQUARES; ALGORITHM;
D O I
10.1007/s13160-022-00505-2
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Consider using the right-preconditioned GMRES (AB-GMRES) for obtaining the minimum-norm solution of inconsistent underdetermined systems of linear equations. Morikuni (Ph.D. thesis, 2013) showed that for some inconsistent and ill-conditioned problems, the iterates may diverge. This is mainly because the Hessenberg matrix in the GMRES method becomes very ill-conditioned so that the backward substitution of the resulting triangular system becomes numerically unstable. We propose a stabilized GMRES based on solving the normal equations corresponding to the above triangular system using the standard Cholesky decomposition. This has the effect of shifting upwards the tiny singular values of the Hessenberg matrix which lead to an inaccurate solution. We analyze why the method works. Numerical experiments show that the proposed method is robust and efficient, not only for applying AB-GMRES to underdetermined systems, but also for applying GMRES to severely ill-conditioned range-symmetric systems of linear equations.
引用
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页码:717 / 751
页数:35
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