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 618 articles , 3 standard articles )

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  1. Wang, Fajie; Fan, Chia-Ming; Hua, Qingsong; Gu, Yan: Localized MFS for the inverse Cauchy problems of two-dimensional Laplace and biharmonic equations (2020)
  2. Ayala, Alan; Claeys, Xavier; Grigori, Laura: ALORA: affine low-rank approximations (2019)
  3. Che, Maolin; Wei, Yimin: Randomized algorithms for the approximations of Tucker and the tensor train decompositions (2019)
  4. Chung, Julianne; Gazzola, Silvia: Flexible Krylov methods for (\ell_p) regularization (2019)
  5. Ding, Liang; Han, Weimin: (\alpha\ell_1-\beta\ell_2) regularization for sparse recovery (2019)
  6. Dong, Yiqiu; Hansen, Per Christian; Hochstenbach, Michiel E.; Brogaard Riis, Nicolai André: Fixing nonconvergence of algebraic iterative reconstruction with an unmatched backprojector (2019)
  7. Fung, Samy Wu; Ruthotto, Lars: An uncertainty-weighted asynchronous ADMM method for parallel PDE parameter estimation (2019)
  8. Gazzola, Silvia; Hansen, Per Christian; Nagy, James G.: IR tools: a MATLAB package of iterative regularization methods and large-scale test problems (2019)
  9. Gazzola, Silvia; Noschese, Silvia; Novati, Paolo; Reichel, Lothar: Arnoldi decomposition, GMRES, and preconditioning for linear discrete ill-posed problems (2019)
  10. Gazzola, Silvia; Sabaté Landman, Malena: Flexible GMRES for total variation regularization (2019)
  11. Gerth, Daniel: Using Landweber iteration to quantify source conditions - a numerical study (2019)
  12. Haddock, Jamie; Needell, Deanna: Randomized projection methods for Linear systems with arbitrarily large sparse corruptions (2019)
  13. Hämarik, Uno; Kangro, Urve; Kindermann, Stefan; Raik, Kemal: Semi-heuristic parameter choice rules for Tikhonov regularisation with operator perturbations (2019)
  14. Haußer, Frank; Luchko, Yuri: Mathematical modelling with MATLAB and Octave. A practice-oriented introduction (2019)
  15. Hernandez-Montero, Eduardo; Fraguela-Collar, Andres; Henry, Jacques: An optimal quasi solution for the Cauchy problem for Laplace equation in the framework of inverse ECG (2019)
  16. Huang, Guangxin; Reichel, Lothar; Yin, Feng: On the choice of subspace for large-scale Tikhonov regularization problems in general form (2019)
  17. Hu, Yunyi; Nagy, James G.; Zhang, Jianjun; Andersen, Martin S.: Nonlinear optimization for mixed attenuation polyenergetic image reconstruction (2019)
  18. 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)
  19. Lu, Zhong-Rong; Wang, Li: Cavity identification in elastic structures by explicit domain mapping and boundary mode sensitivity analysis (2019)
  20. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv

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