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 124 articles , 2 standard articles )

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  1. Fox, Colin; Norton, Richard A.: Fast sampling in a linear-Gaussian inverse problem (2016)
  2. Goza, Andres; Liska, Sebastian; Morley, Benjamin; Colonius, Tim: Accurate computation of surface stresses and forces with immersed boundary methods (2016)
  3. Huan, Guoqiang; Li, Ying; Song, Zhanjie: A novel robust principal component analysis method for image and video processing. (2016)
  4. Liu, J.J.; Yamamoto, M.; Yan, L.L.: On the reconstruction of unknown time-dependent boundary sources for time fractional diffusion process by distributing measurement (2016)
  5. Niinimäki, Kati; Lassas, Matti; Hämäläinen, Keijo; Kallonen, Aki; Kolehmainen, Ville; Niemi, Esa; Siltanen, Samuli: Multiresolution parameter choice method for total variation regularized tomography (2016)
  6. Pérez, F.; Pérez, A.; Rodríguez, M.; Magdaleno, E.: Lightfield recovery from its focal stack (2016)
  7. Revunova, E.G.: Model selection criteria for a linear model to solve discrete ill-posed problems on the basis of singular decomposition and random projection (2016)
  8. Wei, Yimin; Xie, Pengpeng; Zhang, Liping: Tikhonov regularization and randomized GSVD (2016)
  9. Wen, YouWei; Chan, Raymond Honfu; Zeng, TieYong: Primal-dual algorithms for total variation based image restoration under Poisson noise (2016)
  10. Abdelaziz, Batoul; El Badia, Abdellatif; El Hajj, Ahmad: Direct algorithm for multipolar sources reconstruction (2015)
  11. Ahusborde, E.; Azaïez, M.; Ben Belgacem, F.; Palomo Del Barrio, E.: Mercer’s spectral decomposition for the characterization of thermal parameters (2015)
  12. Berisha, Sebastian; Nagy, James G.; Plemmons, Robert J.: Estimation of atmospheric PSF parameters for hyperspectral imaging. (2015)
  13. Hansen, Jakob K.; Hogue, Jarom D.; Sander, Grant K.; Renaut, Rosemary A.; Popat, Sudeep C.: Non-negatively constrained least squares and parameter choice by the residual periodogram for the inversion of electrochemical impedance spectroscopy data (2015)
  14. Lanza, Alessandro; Morigi, Serena; Sgallari, Fiorella: Variational image restoration with constraints on noise whiteness (2015)
  15. Matonoha, C.; Papáček, Š.: On the connection and equivalence of two methods for solving an ill-posed inverse problem based on FRAP data (2015)
  16. Mayer, Philipp A.; Packham, Natalie; Schmidt, Wolfgang M.: Static hedging under maturity mismatch (2015)
  17. Öktem, Ozan: Mathematics of electron tomography (2015)
  18. Revunova, E.G.: Analytical study of error components for solving discrete ill-posed problems using random projections (2015)
  19. Rundell, William: Recovering the density of a string from only lowest frequency data (2015)
  20. Spantini, Alessio; Solonen, Antti; Cui, Tiangang; Martin, James; Tenorio, Luis; Marzouk, Youssef: Optimal low-rank approximations of Bayesian linear inverse problems (2015)

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