Large-scale Tikhonov regularization via reduction by orthogonal projection

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Abstract

This paper presents a new approach to computing an approximate solution of Tikhonov-regularized large-scale ill-posed least-squares problems with a general regularization matrix. The iterative method applies a sequence of projections onto generalized Krylov subspaces. A suitable value of the regularization parameter is determined by the discrepancy principle. © 2011 Elsevier Inc. All rights reserved.

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Lampe, J., Reichel, L., & Voss, H. (2012). Large-scale Tikhonov regularization via reduction by orthogonal projection. Linear Algebra and Its Applications, 436(8), 2845–2865. https://doi.org/10.1016/j.laa.2011.07.019

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