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   https://doi.org/10.1007/s11075-025-02072-2  

   Buccini, Alessandro; Gazzola, Silvia; Onisk, Lucas; Pasha, Mirjeta; Reichel, Lothar (May 2025, Numerical Algorithms)

   Abstract Tikhonov regularization is commonly used in the solution of linear discrete ill-posed problems. It is known that iterated Tikhonov regularization often produces approximate solutions of higher quality than (standard) Tikhonov regularization. This paper discusses iterated Tikhonov regularization for large-scale problems with a general regularization matrix. Specifically, the original problem is reduced to small size by application of a fairly small number of steps of the Arnoldi or Golub-Kahan processes, and iterated Tikhonov is applied to the reduced problem. The regularization parameter is determined by using an extension of a technique first described by Donatelli and Hanke for quite special coefficient matrices. Convergence of the method is established and computed examples illustrate its performance. 

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2. Generalized singular value decomposition with iterated Tikhonov regularization

   https://doi.org/10.1016/j.cam.2019.05.024  

   Buccini, Alessandro; Pasha, Mirjeta; Reichel, Lothar (August 2020, Journal of Computational and Applied Mathematics)
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   https://doi.org/10.1016/J.CAM.2018.04.049  

   Park, Y.; Reichel, L.; Rodriguez, G.; Yu, X. (December 2018, Journal of Computational and Applied Mathematics)
4. Relaxation spectra using nonlinear Tikhonov regularization with a Bayesian criterion

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   Shanbhag, Sachin (August 2020, Rheologica Acta)

   null (Ed.)
5. On the choice of subspace for large-scale Tikhonov regularization problems in general form

   https://doi.org/10.1007/s11075-018-0534-y  

   Huang, Guangxin; Reichel, Lothar; Yin, Feng (May 2019, Numerical Algorithms)