An inner-outer regularizing method for ill-posed problems
Paola Favati Grazia Lotti Ornella Menchi Francesco Romani
Inverse Problems & Imaging 2014, 8(2): 409-420 doi: 10.3934/ipi.2014.8.409
Conjugate Gradient is widely used as a regularizing technique for solving linear systems with ill-conditioned coefficient matrix and right-hand side vector perturbed by noise. It enjoys a good convergence rate and computes quickly an iterate, say $x_{k_{opt}}$, which minimizes the error with respect to the exact solution. This behavior can be a disadvantage in the regularization context, because also the high-frequency components of the noise enter quickly the computed solution, leading to a difficult detection of $k_{opt}$ and to a sharp increase of the error after the $k_{opt}$th iteration. In this paper we propose an inner-outer algorithm based on a sequence of restarted Conjugate Gradients, with the aim of overcoming this drawback. A numerical experimentation validates the effectiveness of the proposed algorithm.
keywords: iterative methods generalized cross validation inner-outer algorithms. Regularization problems conjugate gradient

Year of publication

Related Authors

Related Keywords

[Back to Top]