Decomposition methods for large linear discrete ill-posed problems
Date of Original Version
The solution of large linear discrete ill-posed problems by iterative methods continues to receive considerable attention. This paper presents decomposition methods that split the solution space into a Krylov subspace that is determined by the iterative method and an auxiliary subspace that can be chosen to help represent pertinent features of the solution. Decomposition is well suited for use with the GMRES, RRGMRES, and LSQR iterative schemes. © 2006 Elsevier B.V. All rights reserved.
Publication Title, e.g., Journal
Journal of Computational and Applied Mathematics
Baglama, James, and Lothar Reichel. "Decomposition methods for large linear discrete ill-posed problems." Journal of Computational and Applied Mathematics 198, 2 (2007): 332-343. doi: 10.1016/j.cam.2005.09.025.