Title
Parameter determination for Tikhonov regularization problems in general form.
Abstract
Tikhonov regularization is one of the most popular methods for computing an approximate solution of linear discrete ill-posed problems with error-contaminated data. A regularization parameter λ>0 balances the influence of a fidelity term, which measures how well the data are approximated, and of a regularization term, which dampens the propagation of the data error into the computed approximate solution. The value of the regularization parameter is important for the quality of the computed solution: a too large value of λ>0 gives an over-smoothed solution that lacks details that the desired solution may have, while a too small value yields a computed solution that is unnecessarily, and possibly severely, contaminated by propagated error. When a fairly accurate estimate of the norm of the error in the data is known, a suitable value of λ often can be determined with the aid of the discrepancy principle. This paper is concerned with the situation when the discrepancy principle cannot be applied. It then can be quite difficult to determine a suitable value of λ. We consider the situation when the Tikhonov regularization problem is in general form, i.e., when the regularization term is determined by a regularization matrix different from the identity, and describe an extension of the COSE method for determining the regularization parameter λ in this situation. This method has previously been discussed for Tikhonov regularization in standard form, i.e., for the situation when the regularization matrix is the identity. It is well known that Tikhonov regularization in general form, with a suitably chosen regularization matrix, can give a computed solution of higher quality than Tikhonov regularization in standard form.
Year
DOI
Venue
2018
10.1016/j.cam.2018.04.049
Journal of Computational and Applied Mathematics
Keywords
Field
DocType
65F10,65F22,65R30
Tikhonov regularization,Propagation of uncertainty,Matrix (mathematics),Mathematical analysis,Regularization (mathematics),Approximate solution,Mathematics
Journal
Volume
ISSN
Citations 
343
0377-0427
2
PageRank 
References 
Authors
0.40
10
4
Name
Order
Citations
PageRank
Y. Park120.40
Lothar Reichel245395.02
Giuseppe Rodriguez319729.43
X. Yu420.40