Abstract
Lothar Reichel and his collaborators proposed several iterative algorithms that augment the underlying Krylov subspace with an additional low-dimensional subspace in order to produce improved regularized solutions. We take a closer look at this approach and investigate a particular Regularized Range-Restricted GMRES method, R3GMRES, with a subspace that represents prior information about the solution. We discuss the implementation of this approach and demonstrate its advantage by means of several test problems.
Original language | English |
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Journal | Electronic Transactions on Numerical Analysis |
Volume | 42 |
Pages (from-to) | 136-146 |
ISSN | 1068-9613 |
Publication status | Published - 2014 |
Keywords
- Inverse problems
- Regularizing iterations
- Large-scale problems
- Prior information