Arnoldi decomposition, GMRES, and preconditioning for linear discrete ill-posed problems
For researchers solving inverse problems, this paper provides insights into the limitations of GMRES and offers practical remedies, though it is an incremental contribution.
The paper investigates why GMRES performs poorly for linear discrete ill-posed problems and proposes preconditioning strategies to improve its performance. It also discusses Arnoldi-Tikhonov and Arnoldi-TSVD methods as alternatives, with numerical examples demonstrating their properties.
GMRES is one of the most popular iterative methods for the solution of large linear systems of equations that arise from the discretization of linear well-posed problems, such as Dirichlet boundary value problems for elliptic partial differential equations. The method is also applied to iteratively solve linear systems of equations that are obtained by discretizing linear ill-posed problems, such as many inverse problems. However, GMRES does not always perform well when applied to the latter kind of problems. This paper seeks to shed some light on reasons for the poor performance of GMRES in certain situations, and discusses some remedies based on specific kinds of preconditioning. The standard implementation of GMRES is based on the Arnoldi process, which also can be used to define a solution subspace for Tikhonov or TSVD regularization, giving rise to the Arnoldi-Tikhonov and Arnoldi-TSVD methods, respectively. The performance of the GMRES, the Arnoldi-Tikhonov, and the Arnoldi-TSVD methods is discussed. Numerical examples illustrate properties of these methods.