UTV

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 189 articles , 2 standard articles )

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  1. Aminikhah, Hossein; Yousefi, Mahsa: A special generalized HSS method for discrete ill-posed problems (2018)
  2. Aminikhah, H.; Yousefi, M.: Preconditioned RRGMRES for discrete ill-posed problems (2018)
  3. Calvetti, D.; Pitolli, F.; Somersalo, E.; Vantaggi, B.: Bayes meets Krylov: statistically inspired preconditioners for CGLS (2018)
  4. Da Silva, Nuno V.; Yao, Gang: Wavefield reconstruction inversion with a multiplicative cost function (2018)
  5. Deif, Sarah A.; Grace, Said R.: Fast iterative refinement method for mixed systems of integral and fractional integro-differential equations (2018)
  6. Dickstein, Flávio; Goldfeld, Paulo; Pfeiffer, Gustavo T.; Pinto, Renan V.: Truncated conjugate gradient and improved LBFGS and TSVD for history matching (2018)
  7. Jia, Zhongxiao; Yang, Yanfei: Modified truncated randomized singular value decomposition (MTRSVD) algorithms for large scale discrete ill-posed problems with general-form regularization (2018)
  8. Lee, Tsung-Lin; Li, Tien-Yien; Zeng, Zhonggang: RankRev: a Matlab package for computing the numerical rank and updating/downdating (2018)
  9. Novati, P.: A convergence result for some Krylov-Tikhonov methods in Hilbert spaces (2018)
  10. Revunova, E. G.: Increasing the accuracy of solving discrete ill-posed problems by the random projection method (2018)
  11. Sun, Jie; Quevedo, Fernando J.; Bollt, Erik: Bayesian optical flow with uncertainty quantification (2018)
  12. Zibetti, Marcelo V. W.; Lin, Chuan; Herman, Gabor T.: Total variation superiorized conjugate gradient method for image reconstruction (2018)
  13. Arcucci, Rossella; D’Amore, Luisa; Pistoia, Jenny; Toumi, Ralf; Murli, Almerico: On the variational data assimilation problem solving and sensitivity analysis (2017)
  14. Bai, Zhong-Zhi; Buccini, Alessandro; Hayami, Ken; Reichel, Lothar; Yin, Jun-Feng; Zheng, Ning: Modulus-based iterative methods for constrained Tikhonov regularization (2017)
  15. Berntsson, F.; Kozlov, V. A.; Mpinganzima, L.; Turesson, B. O.: Iterative Tikhonov regularization for the Cauchy problem for the Helmholtz equation (2017)
  16. Callahan, Margaret; Calvetti, Daniela; Somersalo, Erkki: Beyond the model limit: parameter inference across scales (2017)
  17. Calvetti, D.; Pitolli, F.; Prezioso, J.; Somersalo, E.; Vantaggi, B.: Priorconditioned CGLS-based quasi-MAP estimate, statistical stopping rule, and ranking of priors (2017)
  18. Chapko, R.; Johansson, B. T.: Boundary-integral approach to the numerical solution of the Cauchy problem for the Laplace equation (2017)
  19. Chávez, Carlos Eduardo; Alonso-Atienza, Felipe; Álvarez, Diego: The use of a simple model in the inverse characterization of cardiac ischemic regions (2017)
  20. Chung, Matthias; Krueger, Justin; Pop, Mihai: Identification of microbiota dynamics using robust parameter estimation methods (2017)

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