Matrix decompositions for Tikhonov regularization
Tikhonov regularization is a popular method for solving linear discrete ill-posed problems with error-contaminated data. This method replaces the given linear discrete ill-posed problem by a penalized least-squares problem. The choice of the regularization matrix in the penalty term is important. We...
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Veröffentlicht in: | Electronic transactions on numerical analysis 2014-12, Vol.43, p.223 |
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Sprache: | eng |
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Zusammenfassung: | Tikhonov regularization is a popular method for solving linear discrete ill-posed problems with error-contaminated data. This method replaces the given linear discrete ill-posed problem by a penalized least-squares problem. The choice of the regularization matrix in the penalty term is important. We are concerned with the situation when this matrix is of fairly general form. The penalized least-squares problem can be conveniently solved with the aid of the generalized singular value decomposition, provided that the size of the problem is not too large. However, it is impractical to use this decomposition for large-scale penalized least-squares problems. This paper describes new matrix decompositions that are well suited for the solution of large-scale penalized least-square problems that arise in Tikhonov regularization with a regularization matrix of general form. Key words. ill-posed problem, matrix decomposition, generalized Krylov method, Tikhonov regularization. AMS subject classifications. 65F22, 65F10, 65R30 |
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ISSN: | 1068-9613 1097-4067 |