Regularization tools

Regularization Tools: A MATLAB package for Analysis and Solution of Discrete Ill-Posed Problems. Version 4.1. By means of the routines in this package, the user can experiment with different regularization strategies. The package also includes 12 test problems. Requires Matlab Version 7.3. The manual and more details can be found at pch/Regutools/

References in zbMATH (referenced in 586 articles , 3 standard articles )

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  1. Gazzola, Silvia; Hansen, Per Christian; Nagy, James G.: IR tools: a MATLAB package of iterative regularization methods and large-scale test problems (2019)
  2. Lang, Oliver; Kovács, Péter; Motz, Christian; Huemer, Mario; Berer, Thomas; Burgholzer, Peter: A linear state space model for photoacoustic imaging in an acoustic attenuating media (2019)
  3. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  4. Ramlau, Ronny; Reichel, Lothar: Error estimates for Arnoldi-Tikhonov regularization for ill-posed operator equations (2019)
  5. Ahmed, Elyes; Abda, Amel Ben: The sub-Cauchy-Stokes problem: solvability issues and Lagrange multiplier methods with artificial boundary conditions (2018)
  6. Aminikhah, Hossein; Yousefi, Mahsa: A special generalized HSS method for discrete ill-posed problems (2018)
  7. Aminikhah, H.; Yousefi, M.: Preconditioned RRGMRES for discrete ill-posed problems (2018)
  8. Askour, Omar; Tri, Abdeljalil; Braikat, Bouazza; Zahrouni, Hamid; Potier-Ferry, Michel: Method of fundamental solutions and high order algorithm to solve nonlinear elastic problems (2018)
  9. Bardsley, Johnathan M.: Computational uncertainty quantification for inverse problems (2018)
  10. Bazán, Fermín S. V.; Boos, Everton: Schultz matrix iteration based method for stable solution of discrete ill-posed problems (2018)
  11. Bentbib, A. H.; El Guide, M.; Jbilou, K.; Onunwor, E.; Reichel, L.: Solution methods for linear discrete ill-posed problems for color image restoration (2018)
  12. Brown, D. Andrew; Saibaba, Arvind; Vallélian, Sarah: Low-rank independence samplers in hierarchical Bayesian inverse problems (2018)
  13. Calvetti, D.; Pitolli, F.; Somersalo, E.; Vantaggi, B.: Bayes meets Krylov: statistically inspired preconditioners for CGLS (2018)
  14. Chung, Julianne; Saibaba, Arvind K.; Brown, Matthew; Westman, Erik: Efficient generalized Golub-Kahan based methods for dynamic inverse problems (2018)
  15. Cogar, Samuel: A modified transmission eigenvalue problem for scattering by a partially coated crack (2018)
  16. Da Silva, Nuno V.; Yao, Gang: Wavefield reconstruction inversion with a multiplicative cost function (2018)
  17. Deif, Sarah A.; Grace, Said R.: Fast iterative refinement method for mixed systems of integral and fractional integro-differential equations (2018)
  18. Garvey, Larissa; Meng, Chang; Nagy, James G.: Singular value decomposition approximation via Kronecker summations for imaging applications (2018)
  19. Gockenbach, Mark S.; Gorgin, Elaheh: On the convergence of a heuristic parameter choice rule for Tikhonov regularization (2018)
  20. Grigori, Laura; Cayrols, Sebastien; Demmel, James W.: Low rank approximation of a sparse matrix based on LU factorization with column and row tournament pivoting (2018)

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