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|Type:||Artigo de periódico|
|Title:||On A Generalization Of Regińska's Parameter Choice Rule And Its Numerical Realization In Large-scale Multi-parameter Tikhonov Regularization|
|Author:||Viloche Bazan F.S.|
|Abstract:||A crucial problem concerning Tikhonov regularization is the proper choice of the regularization parameter. This paper deals with a generalization of a parameter choice rule due to Regińska (1996) , analyzed and algorithmically realized through a fast fixed-point method in Bazán (2008) , which results in a fixed-point method for multi-parameter Tikhonov regularization called MFP. Like the single-parameter case, the algorithm does not require any information on the noise level. Further, combining projection over the Krylov subspace generated by the Golub-Kahan bidiagonalization (GKB) algorithm and the MFP method at each iteration, we derive a new algorithm for large-scale multi-parameter Tikhonov regularization problems. The performance of MFP when applied to well known discrete ill-posed problems is evaluated and compared with results obtained by the discrepancy principle. The results indicate that MFP is efficient and competitive. The efficiency of the new algorithm on a super-resolution problem is also illustrated. © 2012 Elsevier Inc. All rights reserved.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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