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 660 articles , 3 standard articles )

Showing results 1 to 20 of 660.
Sorted by year (citations)

1 2 3 ... 31 32 33 next

  1. Bentbib, A. H.; El Guide, M.; Jbilou, K.: A generalized matrix Krylov subspace method for TV regularization (2020)
  2. Benvenuto, Federico; Jin, Bangti: A parameter choice rule for Tikhonov regularization based on predictive risk (2020)
  3. Buccini, Alessandro; Donatelli, Marco: A multigrid frame based method for image deblurring (2020)
  4. Buccini, Alessandro; Park, Yonggi; Reichel, Lothar: Comparison of a-posteriori parameter choice rules for linear discrete ill-posed problems (2020)
  5. Buccini, Alessandro; Pasha, Mirjeta; Reichel, Lothar: Generalized singular value decomposition with iterated Tikhonov regularization (2020)
  6. Cheng, Jin; Ke, Yufei; Wei, Ting: The backward problem of parabolic equations with the measurements on a discrete set (2020)
  7. Consolini, Luca; Locatelli, Marco; Wang, Jiulin; Xia, Yong: Efficient local search procedures for quadratic fractional programming problems (2020)
  8. Cui, Jingjing; Peng, Guohua; Lu, Quan; Huang, Zhengge: A special modified Tikhonov regularization matrix for discrete ill-posed problems (2020)
  9. Effland, Alexander; Kobler, Erich; Kunisch, Karl; Pock, Thomas: Variational networks: an optimal control approach to early stopping variational methods for image restoration (2020)
  10. Fika, Paraskevi; Mitrouli, Marilena; Roupa, Paraskevi; Triantafyllou, Dimitrios: The e-MoM approach for approximating matrix functionals (2020)
  11. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  12. Gazzola, Silvia; Novati, Paolo: Some transpose-free CG-like solvers for nonsymmetric ill-posed problems (2020)
  13. Jia, Zhongxiao: Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems (2020)
  14. Kindermann, Stefan; Raik, Kemal: Convergence of heuristic parameter choice rules for convex Tikhonov regularization (2020)
  15. Kindermann, Stefan; Raik, Kemal: A simplified L-curve method as error estimator (2020)
  16. Koukouvinos, Christos; Mitrouli, Marilena; Turek, Ondřej: Efficient estimates in regression models with highly correlated covariates (2020)
  17. Mead, J.: ( \chi^2) test for total variation regularization parameter selection (2020)
  18. Mohammady, Somaieh; Eslahchi, M. R.: Extension of Tikhonov regularization method using linear fractional programming (2020)
  19. Najafi-Kalyani, Mehdi; Beik, Fatemeh Panjeh Ali; Jbilou, Khalide: On global iterative schemes based on Hessenberg process for (ill-posed) Sylvester tensor equations (2020)
  20. Radmanesh, Mehdi; Ebadi, M. J.: A local mesh-less collocation method for solving a class of time-dependent fractional integral equations: 2D fractional evolution equation (2020)

1 2 3 ... 31 32 33 next