A rational conjugate gradient method for linear ill-conditioned problems
Abstract
We consider linear ill-conditioned operator equations in a Hilbert space setting. Motivated by the aggregation method, we consider approximate solutions constructed from linear combinations of Tikhonov regularization, which amounts to finding solutions in a rational Krylov space. By mixing these with usual Krylov spaces, we consider least-squares problem in these mixed rational spaces. Applying the Arnoldi method leads to a sparse, pentadiagonal representation of the forward operator, and we introduce the Lanczos method for solving the least-squares problem by factorizing this matrix. Finally, we present an equivalent conjugate-gradient-type method that does not rely on explicit orthogonalization but uses short-term recursions and Tikhonov regularization in each second step. We illustrate the convergence and regularization properties by some numerical examples.
Cite
@article{arxiv.2306.03670,
title = {A rational conjugate gradient method for linear ill-conditioned problems},
author = {Stefan Kindermann and Werner Zellinger},
journal= {arXiv preprint arXiv:2306.03670},
year = {2023}
}