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.
|Journal||Electronic Transactions on Numerical Analysis|
|Publication status||Published - 2014|
- Inverse problems
- Regularizing iterations
- Large-scale problems
- Prior information