Abstract
Tikhonov regularization using SVD (Singular Value Decomposition) is an effective method for discrete ill-posed linear operator equations. We propose a new regularization method using Rank Revealing QR Factorization which requires far less computational cost than that of SVD. It is important to choose regularization parameter to obtain a good approximate solution for the equation. For the choice of the regularization parameter, Generalized cross-validation (GCV) and the L-curve method are often used.We apply these two methods to the regularization using rank revealing QR factorization to produce a reasonable solution.
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References
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© 2001 Springer-Verlag Berlin Heidelberg
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Nakata, S., Kitagawa, T., Hosoda, Y. (2001). Regularization Method by Rank Revealing QR Factorization and Its Optimization. In: Vulkov, L., Yalamov, P., Waśniewski, J. (eds) Numerical Analysis and Its Applications. NAA 2000. Lecture Notes in Computer Science, vol 1988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45262-1_72
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DOI: https://doi.org/10.1007/3-540-45262-1_72
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