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 250 articles , 2 standard articles )

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  1. Gratton, S.; Simon, E.; Toint, Ph. L.: An algorithm for the minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity (2021)
  2. Huang, Jinzhi; Jia, Zhongxiao: On choices of formulations of computing the generalized singular value decomposition of a large matrix pair (2021)
  3. Luiken, Nick; van Leeuwen, Tristan: Relaxed regularization for linear inverse problems (2021)
  4. Tavares, Camila A.; Santos, Taináh M. R.; Lemes, Nelson H. T.; dos Santos, José P. C.; Ferreira, José C.; Braga, João P.: Solving ill-posed problems faster using fractional-order Hopfield neural network (2021)
  5. van Lith, Bart S.; Hansen, Per Christian; Hochstenbach, Michiel E.: A twin error gauge for Kaczmarz’s iterations (2021)
  6. Zhang, Jianjun; Nagy, James G.: An effective alternating direction method of multipliers for color image restoration (2021)
  7. Zhao, Mingchao; Wen, You-Wei; Ng, Michael; Li, Hongwei: A nonlocal low rank model for Poisson noise removal (2021)
  8. Adcock, Ben; Huybrechs, Daan: Approximating smooth, multivariate functions on irregular domains (2020)
  9. Barroso, G.; Seoane, M.; Gil, A. J.; Ledger, P. D.; Mallett, M.; Huerta, A.: A staggered high-dimensional proper generalised decomposition for coupled magneto-mechanical problems with application to MRI scanners (2020)
  10. Brown, Richard D.; Bardsley, Johnathan M.; Cui, Tiangang: Semivariogram methods for modeling Whittle-Matérn priors in Bayesian inverse problems (2020)
  11. Buccini, A.; Pasha, M.; Reichel, L.: Modulus-based iterative methods for constrained (\ell_p)-(\ell_q) minimization (2020)
  12. Buryachenko, Valeriy A.: Variational principles and generalized Hill’s bounds in micromechanics of linear peridynamic random structure composites (2020)
  13. Chang, Xiao-Wen; Kang, Peng; Titley-Peloquin, David: Error bounds for computed least squares estimators (2020)
  14. Deidda, Gian Piero; Díaz de Alba, Patricia; Rodriguez, Giuseppe; Vignoli, Giulio: Inversion of multiconfiguration complex EMI data with minimum gradient support regularization: a case study (2020)
  15. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  16. Giusti, Marc; Yakoubsohn, Jean-Claude: Numerical approximation of multiple isolated roots of analytical systems (2020)
  17. Jia, Zhongxiao: Regularization properties of Krylov iterative solvers CGME and LSMR for linear discrete ill-posed problems with an application to truncated randomized SVDs (2020)
  18. Jia, Zhongxiao: The low rank approximations and Ritz values in LSQR for linear discrete ill-posed problem (2020)
  19. Jia, Zhongxiao: Regularization properties of LSQR for linear discrete ill-posed problems in the multiple singular value case and best, near best and general low rank approximations (2020)
  20. Jia, Zhongxiao: Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems (2020)

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