UTV Expansin pack: Special-purpose rank-revealing algorithms This collection of Matlab 7.0 software supplements and complements the package UTV Tools from 1999, and includes implementations of special-purpose rank-revealing algorithms developed since the publication of the original package. We provide algorithms for computing and modifying symmetric rank-revealing VSV decompositions, we expand the algorithms for the ULLV decomposition of a matrix pair to handle interference-type problems with a rank-deficient covariance matrix, and we provide a robust and reliable Lanczos algorithm which -- despite its simplicity is -- able to capture all the dominant singular values of a sparse or structured matrix. These new algorithms have applications in signal processing, optimization and LSI information retrieval. (Source: http://plato.asu.edu)

References in zbMATH (referenced in 178 articles , 2 standard articles )

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  1. Aminikhah, H.; Yousefi, M.: Preconditioned RRGMRES for discrete ill-posed problems (2018)
  2. Calvetti, D.; Pitolli, F.; Somersalo, E.; Vantaggi, B.: Bayes meets Krylov: statistically inspired preconditioners for CGLS (2018)
  3. Da Silva, Nuno V.; Yao, Gang: Wavefield reconstruction inversion with a multiplicative cost function (2018)
  4. Jia, Zhongxiao; Yang, Yanfei: Modified truncated randomized singular value decomposition (MTRSVD) algorithms for large scale discrete ill-posed problems with general-form regularization^* (2018)
  5. Lee, Tsung-Lin; Li, Tien-Yien; Zeng, Zhonggang: RankRev: a Matlab package for computing the numerical rank and updating/downdating (2018)
  6. Novati, P.: A convergence result for some Krylov-Tikhonov methods in Hilbert spaces (2018)
  7. Zibetti, Marcelo V. W.; Lin, Chuan; Herman, Gabor T.: Total variation superiorized conjugate gradient method for image reconstruction (2018)
  8. Arcucci, Rossella; D’Amore, Luisa; Pistoia, Jenny; Toumi, Ralf; Murli, Almerico: On the variational data assimilation problem solving and sensitivity analysis (2017)
  9. Bai, Zhong-Zhi; Buccini, Alessandro; Hayami, Ken; Reichel, Lothar; Yin, Jun-Feng; Zheng, Ning: Modulus-based iterative methods for constrained Tikhonov regularization (2017)
  10. Berntsson, F.; Kozlov, V. A.; Mpinganzima, L.; Turesson, B. O.: Iterative Tikhonov regularization for the Cauchy problem for the Helmholtz equation (2017)
  11. Callahan, Margaret; Calvetti, Daniela; Somersalo, Erkki: Beyond the model limit: parameter inference across scales (2017)
  12. Calvetti, D.; Pitolli, F.; Prezioso, J.; Somersalo, E.; Vantaggi, B.: Priorconditioned CGLS-based quasi-MAP estimate, statistical stopping rule, and ranking of priors (2017)
  13. Chávez, Carlos Eduardo; Alonso-Atienza, Felipe; Álvarez, Diego: The use of a simple model in the inverse characterization of cardiac ischemic regions (2017)
  14. Chung, Matthias; Krueger, Justin; Pop, Mihai: Identification of microbiota dynamics using robust parameter estimation methods (2017)
  15. Estatico, Claudio; Gratton, Serge; Lenti, Flavia; Titley-Peloquin, David: A conjugate gradient like method for $p$-norm minimization in functional spaces (2017)
  16. Huang, Yi; Jia, Zhongxiao: On regularizing effects of MINRES and MR-II for large scale symmetric discrete ill-posed problems (2017)
  17. Huang, Yi; Jia, ZhongXiao: Some results on the regularization of LSQR for large-scale discrete ill-posed problems (2017)
  18. Levin, Eitan; Meltzer, Alexander Y.: Estimation of the regularization parameter in linear discrete ill-posed problems using the Picard parameter (2017)
  19. Martínez-Finkelshtein, A.; Ramos-López, D.; Iskander, D. R.: Computation of 2D Fourier transforms and diffraction integrals using Gaussian radial basis functions (2017)
  20. Novati, Paolo: Some properties of the Arnoldi-based methods for linear ill-posed problems (2017)

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