Some properties of range restricted GMRES methods

The GMRES method is one of the most popular iterative schemes for the solution of large linear systems of equations with a square nonsingular matrix. GMRES-type methods also have been applied to the solution of linear discrete ill-posed problems. Computational experience indicates that for the latter problems variants of the standard GMRES method, that require the solution to live in the range of a positive power of the matrix of the linear system of equations to be solved, generally yield more accurate approximations of the desired solution than standard GMRES. This paper investigates properties of these variants of GMRES.

[1]  Maria Rosaria Russo,et al.  On Krylov projection methods and Tikhonov regularization , 2015 .

[2]  Gene H. Golub,et al.  Matrix computations , 1983 .

[3]  Lothar Reichel,et al.  GMRES, L-Curves, and Discrete Ill-Posed Problems , 2002 .

[4]  David L. Phillips,et al.  A Technique for the Numerical Solution of Certain Integral Equations of the First Kind , 1962, JACM.

[5]  Lothar Reichel,et al.  Application of denoising methods to regularizationof ill-posed problems , 2014, Numerical Algorithms.

[6]  Hassane Sadok,et al.  Algorithms for range restricted iterative methods for linear discrete ill-posed problems , 2012, Numerical Algorithms.

[7]  P. Hansen,et al.  Noise propagation in regularizing iterations for image deblurring , 2008 .

[8]  C. B. Shaw,et al.  Improvement of the resolution of an instrument by numerical solution of an integral equation , 1972 .

[9]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[10]  Qiang Ye,et al.  Breakdown-free GMRES for Singular Systems , 2005, SIAM J. Matrix Anal. Appl..

[11]  Hassane Sadok,et al.  Analysis of the convergence of the minimal and the orthogonal residual methods , 2005, Numerical Algorithms.

[12]  Paolo Novati,et al.  Automatic parameter setting for Arnoldi-Tikhonov methods , 2014, J. Comput. Appl. Math..

[13]  Claude Brezinski,et al.  Error estimates for linear systems with applications to regularization , 2008, Numerical Algorithms.

[14]  Lothar Reichel,et al.  Old and new parameter choice rules for discrete ill-posed problems , 2013, Numerical Algorithms.

[15]  Lothar Reichel,et al.  On the generation of Krylov subspace bases , 2012 .

[16]  Lothar Reichel,et al.  On the Choice of Subspace for Iterative Methods for Linear Discrete Ill-Posed Problems , 2001 .

[17]  M. Matinfar,et al.  GMRES implementations and residual smoothing techniques for solving ill-posed linear systems , 2012, Comput. Math. Appl..

[18]  M. Baart The Use of Auto-correlation for Pseudo-rank Determination in Noisy III-conditioned Linear Least-squares Problems , 1982 .

[19]  Per Christian Hansen,et al.  Regularization Tools version 4.0 for Matlab 7.3 , 2007, Numerical Algorithms.

[20]  H. Engl,et al.  Regularization of Inverse Problems , 1996 .

[21]  Lothar Reichel,et al.  Restoration of images with spatially variant blur by the GMRES method , 2000, SPIE Optics + Photonics.

[22]  Lothar Reichel,et al.  A family of range restricted iterative methods for linear discrete ill-posed problems , 2013 .

[23]  Lothar Reichel,et al.  On the regularizing properties of the GMRES method , 2002, Numerische Mathematik.

[24]  Michiel E. Hochstenbach,et al.  Discrete ill-posed least-squares problems with a solution norm constraint , 2012 .

[25]  Hassane Sadok,et al.  Implementations of range restricted iterative methods for linear discrete ill-posed problems , 2012 .

[26]  D. Calvetti,et al.  Tikhonov regularization and the L-curve for large discrete ill-posed problems , 2000 .

[27]  H. Walker,et al.  GMRES On (Nearly) Singular Systems , 1997, SIAM J. Matrix Anal. Appl..