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 236 articles , 2 standard articles )

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  1. 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)
  2. Adcock, Ben; Huybrechs, Daan: Approximating smooth, multivariate functions on irregular domains (2020)
  3. Buccini, A.; Pasha, M.; Reichel, L.: Modulus-based iterative methods for constrained (\ell_p)-(\ell_q) minimization (2020)
  4. Buryachenko, Valeriy A.: Variational principles and generalized Hill’s bounds in micromechanics of linear peridynamic random structure composites (2020)
  5. Chang, Xiao-Wen; Kang, Peng; Titley-Peloquin, David: Error bounds for computed least squares estimators (2020)
  6. 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)
  7. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  8. Giusti, Marc; Yakoubsohn, Jean-Claude: Numerical approximation of multiple isolated roots of analytical systems (2020)
  9. 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)
  10. Jia, Zhongxiao: Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems (2020)
  11. Jia, Zhongxiao; Yang, Yanfei: A joint bidiagonalization based iterative algorithm for large scale general-form Tikhonov regularization (2020)
  12. Mohammady, Somaieh; Eslahchi, M. R.: Extension of Tikhonov regularization method using linear fractional programming (2020)
  13. Reichel, Lothar; Sadok, Hassane; Zhang, Wei-Hong: Simple stopping criteria for the LSQR method applied to discrete ill-posed problems (2020)
  14. Webber, James W.; Quinto, Eric Todd; Miller, Eric L.: A joint reconstruction and lambda tomography regularization technique for energy-resolved x-ray imaging (2020)
  15. Zare, Hossein; Hajarian, Masoud: Determination of regularization parameter via solving a multi-objective optimization problem (2020)
  16. Asl, Nazdar Abdollahi; Rostamy, Davood: Identifying an unknown time-dependent boundary source in time-fractional diffusion equation with a non-local boundary condition (2019)
  17. Bao, Jifeng; Yu, Carisa Kwok Wai; Wang, Jinhua; Hu, Yaohua; Yao, Jen-Chih: Modified inexact Levenberg-Marquardt methods for solving nonlinear least squares problems (2019)
  18. Buranay, Suzan C.; Iyikal, Ovgu C.: Approximate Schur-block ILU preconditioners for regularized solution of discrete ill-posed problems (2019)
  19. Buryachenko, Valeriy A.: Interface integral technique for the thermoelasticity of random structure matrix composites (2019)
  20. Cao, Zhifu; Fei, Qingguo; Jiang, Dong; Wu, Shaoqing; Fan, Zhiruo: Model updating of a stitched sandwich panel based on multistage parameter selection (2019)

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