A rational conjugate gradient method for linear ill-conditioned problems

2023/06/06 by Kindermann, Stefan, Zellinger, Werner
#FOS: Mathematics #Numerical Analysis (math.NA)

paper · doi:10.48550/arxiv.2306.03670

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.

Related

• Regularization of linear inverse problems by rational Krylov methods
• A rational Arnoldi approach for ill-conditioned linear systems
• An Arnoldi-based approach to polynomial and rational least squares problems
• Convergence of the gradient method for ill-posed problems
• Conjugate gradient for ill-posed problems: regularization by preconditioning, preconditioning by regularization