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 689 articles , 3 standard articles )

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  1. Bai, Xianglan; Huang, Guang-Xin; Lei, Xiao-Jun; Reichel, Lothar; Yin, Feng: A novel modified TRSVD method for large-scale linear discrete ill-posed problems (2021)
  2. Cen, Xiaoli; Xia, Yong; Yang, Meijia: Semidefinite relaxation for the total least squares problem with Tikhonov-like regularization (2021)
  3. Chen, Xiaotong; Herring, James L.; Nagy, James G.; Xi, Yuanzhe; Yu, Bo: An ADMM-LAP method for total variation myopic deconvolution of adaptive optics retinal images (2021)
  4. Kang, Chuan-gang; Zhou, Heng: The extensions of convergence rates of Kaczmarz-type methods (2021)
  5. Nikazad, T.; Karimpour, M.: Column-oriented algebraic iterative methods for nonnegative constrained least squares problems (2021)
  6. Zhang, Hui; Dai, Hua: The regularizing properties of global GMRES for solving large-scale linear discrete ill-posed problems with several right-hand sides (2021)
  7. Bentbib, A. H.; El Guide, M.; Jbilou, K.: A generalized matrix Krylov subspace method for TV regularization (2020)
  8. Benvenuto, Federico; Jin, Bangti: A parameter choice rule for Tikhonov regularization based on predictive risk (2020)
  9. Buccini, Alessandro; Donatelli, Marco: A multigrid frame based method for image deblurring (2020)
  10. Buccini, Alessandro; Park, Yonggi; Reichel, Lothar: Comparison of a-posteriori parameter choice rules for linear discrete ill-posed problems (2020)
  11. Buccini, Alessandro; Pasha, Mirjeta; Reichel, Lothar: Generalized singular value decomposition with iterated Tikhonov regularization (2020)
  12. Buccini, A.; Pasha, M.; Reichel, L.: Modulus-based iterative methods for constrained (\ell_p)-(\ell_q) minimization (2020)
  13. Cheng, Jin; Ke, Yufei; Wei, Ting: The backward problem of parabolic equations with the measurements on a discrete set (2020)
  14. Consolini, Luca; Locatelli, Marco; Wang, Jiulin; Xia, Yong: Efficient local search procedures for quadratic fractional programming problems (2020)
  15. Cornelis, Jeffrey; Vanroose, W.: Projected Newton method for noise constrained (\ell_p) regularization (2020)
  16. Cueva, Evelyn; Courdurier, Matias; Osses, Axel; Castañeda, Victor; Palacios, Benjamin; Härtel, Steffen: Mathematical modeling for 2D light-sheet fluorescence microscopy image reconstruction (2020)
  17. Cui, Jingjing; Peng, Guohua; Lu, Quan; Huang, Zhengge: A special modified Tikhonov regularization matrix for discrete ill-posed problems (2020)
  18. Deidda, G. P.; Díaz de Alba, P.; Fenu, C.; Lovicu, G.; Rodriguez, G.: FDEMtools: a Matlab package for FDEM data inversion (2020)
  19. Ding, Liang; Han, Weimin: A projected gradient method for (\alpha\ell_1-\beta\ell_2) sparsity regularization (2020)
  20. Effland, Alexander; Kobler, Erich; Kunisch, Karl; Pock, Thomas: Variational networks: an optimal control approach to early stopping variational methods for image restoration (2020)

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