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 http://www2.imm.dtu.dk/ pch/Regutools/


References in zbMATH (referenced in 680 articles , 3 standard articles )

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  1. Kang, Chuan-gang; Zhou, Heng: The extensions of convergence rates of Kaczmarz-type methods (2021)
  2. Bentbib, A. H.; El Guide, M.; Jbilou, K.: A generalized matrix Krylov subspace method for TV regularization (2020)
  3. Benvenuto, Federico; Jin, Bangti: A parameter choice rule for Tikhonov regularization based on predictive risk (2020)
  4. Buccini, Alessandro; Donatelli, Marco: A multigrid frame based method for image deblurring (2020)
  5. Buccini, Alessandro; Park, Yonggi; Reichel, Lothar: Comparison of a-posteriori parameter choice rules for linear discrete ill-posed problems (2020)
  6. Buccini, Alessandro; Pasha, Mirjeta; Reichel, Lothar: Generalized singular value decomposition with iterated Tikhonov regularization (2020)
  7. Buccini, A.; Pasha, M.; Reichel, L.: Modulus-based iterative methods for constrained (\ell_p)-(\ell_q) minimization (2020)
  8. Cheng, Jin; Ke, Yufei; Wei, Ting: The backward problem of parabolic equations with the measurements on a discrete set (2020)
  9. Consolini, Luca; Locatelli, Marco; Wang, Jiulin; Xia, Yong: Efficient local search procedures for quadratic fractional programming problems (2020)
  10. Cornelis, Jeffrey; Vanroose, W.: Projected Newton method for noise constrained (\ell_p) regularization (2020)
  11. Cueva, Evelyn; Courdurier, Matias; Osses, Axel; Castañeda, Victor; Palacios, Benjamin; Härtel, Steffen: Mathematical modeling for 2D light-sheet fluorescence microscopy image reconstruction (2020)
  12. Cui, Jingjing; Peng, Guohua; Lu, Quan; Huang, Zhengge: A special modified Tikhonov regularization matrix for discrete ill-posed problems (2020)
  13. Deidda, G. P.; Díaz de Alba, P.; Fenu, C.; Lovicu, G.; Rodriguez, G.: FDEMtools: a Matlab package for FDEM data inversion (2020)
  14. Ding, Liang; Han, Weimin: A projected gradient method for (\alpha\ell_1-\beta\ell_2) sparsity regularization (2020)
  15. Effland, Alexander; Kobler, Erich; Kunisch, Karl; Pock, Thomas: Variational networks: an optimal control approach to early stopping variational methods for image restoration (2020)
  16. Fika, Paraskevi; Mitrouli, Marilena; Roupa, Paraskevi; Triantafyllou, Dimitrios: The e-MoM approach for approximating matrix functionals (2020)
  17. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  18. Gazzola, Silvia; Kilmer, Misha E.; Nagy, James G.; Semerci, Oguz; Miller, Eric L.: An inner-outer iterative method for edge preservation in image restoration and reconstruction (2020)
  19. Gazzola, Silvia; Novati, Paolo: Some transpose-free CG-like solvers for nonsymmetric ill-posed problems (2020)
  20. Jahn, Tim; Jin, Bangti: On the discrepancy principle for stochastic gradient descent (2020)

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