GKB-FP: An algorithm for large-scale discrete ill-posed problems The authors present a new algorithm for discrete ill-posed problems, which is called GKB-FP. This method exploits the Golub-Kahan bidiagonalization algorithm together with Tikhonov regularization in the generated Krylov subspace. The regularization parameter for the projected problem is chosen by the fixed-point method already presented by the first author. A detailed convergence analysis is provided. The paper is enriched by many numerical results on well-known problems so that the effectiveness of the method appears comparable with other methods already used, and even better.
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References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Bazán, Fermín S.V.; Kleefeld, Andreas; Leem, Koung Hee; Pelekanos, George: Sampling method based projection approach for the reconstruction of 3D acoustically penetrable scatterers (2016)
- Chung, Julianne; Palmer, Katrina: A hybrid LSMR algorithm for large-scale Tikhonov regularization (2015)
- Viloche Bazán, Fermín S.: Simple and efficient determination of the Tikhonov regularization parameter chosen by the generalized discrepancy principle for discrete ill-posed problems (2015)
- Huang, Jie; Huang, Ting-Zhu; Zhao, Xi-Le; Xu, Zong-Ben; Lv, Xiao-Guang: Two soft-thresholding based iterative algorithms for image deblurring (2014) ioport
- Pavani, R.; Caliò, F.: About an artificial time approach for iterative numerical solution of Fredholm integral equations of the first kind (2014)
- Viloche Bazán, Fermín S.; Borges, Leonardo S.; Francisco, Juliano B.: On a generalization of Regińska’s parameter choice rule and its numerical realization in large-scale multi-parameter Tikhonov regularization (2012)
- Viloche Bazán, Fermín S.; Borges, Leonardo S.: GKB-FP: An algorithm for large-scale discrete ill-posed problems (2010)