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arXiv · 2404.08321

Improved parameter selection strategy for the iterated Arnoldi-Tikhonov method

Marco Donatelli·Davide Furchì
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Abstract

The iterated Arnoldi-Tikhonov (iAT) method is a regularization technique particularly suited for solving large-scale ill-posed linear inverse problems. Indeed, it reduces the computational complexity through the projection of the discretized problem into a lower-dimensional Krylov subspace, where the problem is then solved. This paper studies iAT under an additional hypothesis on the discretized operator. It presents a theoretical analysis of the approximation errors, leading to an a posteriori rule for choosing the regularization parameter. Our proposed rule results in more accurate computed approximate solutions compared to the a posteriori rule recently proposed in arXiv:2311.11823. The numerical results confirm the theoretical analysis, providing accurate computed solutions even when the new assumption is not satisfied.

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Marco Donatelli, Davide Furchì. 2024-04-12. Improved parameter selection strategy for the iterated Arnoldi-Tikhonov method. https://arxiv.org/abs/2404.08321

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