Large-scale Tikhonov regularization via reduction by orthogonal projection

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 regular...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Linear algebra and its applications Ročník 436; číslo 8; s. 2845 - 2865
Hlavní autoři: Lampe, Jörg, Reichel, Lothar, Voss, Heinrich
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Inc 15.04.2012
Elsevier
Témata:
Algebra
Discrepancy principle
Exact sciences and technology
General-form Tikhonov regularization
Ill-posedness
Least squares
Linear and multilinear algebra, matrix theory
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical approximation
Sciences and techniques of general use
General-form Tikhonov regularization
Discrepancy principle
65F22
65F30
Ill-posedness
Least squares
65F15
Krylov subspace method
Iterative method
Generalized
Orthogonal projection
III-posedness
Least squares method
Ill posed problem
Eigenvalue problem
Large scale
Regularization
ISSN:0024-3795
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:0024-3795
DOI:10.1016/j.laa.2011.07.019