Tikhonov regularization with MTRSVD method for solving large-scale discrete ill-posed problems

Regularization is possibly the most popular method for solving discrete ill-posed problems, whose solution is less sensitive to the error in the observed vector in the right hand than the original solution. This paper presents a new modified truncated randomized singular value decomposition (TR-MTRS...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of computational and applied mathematics Jg. 405; S. 113969
Hauptverfasser: Huang, Guangxin, Liu, Yuanyuan, Yin, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.05.2022
Schlagworte:
Linear discrete ill-posed problem
MTSVD
Randomized algorithm
Tikhonov regularization
Linear discrete ill-posed problem
Randomized algorithm
Tikhonov regularization
MTSVD
ISSN:0377-0427, 1879-1778
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Regularization is possibly the most popular method for solving discrete ill-posed problems, whose solution is less sensitive to the error in the observed vector in the right hand than the original solution. This paper presents a new modified truncated randomized singular value decomposition (TR-MTRSVD) method for large Tikhonov regularization in standard form. The proposed TR-MTRSVD algorithm introduces the idea of randomized algorithm into the improved truncated singular value decomposition (MTSVD) method to solve large Tikhonov regularization problems. The approximation matrix Ãℓ produced by randomized SVD is replaced by the closest matrix Ãk̃ in a unitarily invariant matrix norm with the same spectral condition number. The regularization parameters are determined by the discrepancy principle. Numerical examples show the effectiveness and efficiency of the proposed TR-MTRSVD algorithm for large Tikhonov regularization problems.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2021.113969