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 122 articles , 2 standard articles )

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  7. Wei, Yimin; Xie, Pengpeng; Zhang, Liping: Tikhonov regularization and randomized GSVD (2016)
  8. Wen, YouWei; Chan, Raymond Honfu; Zeng, TieYong: Primal-dual algorithms for total variation based image restoration under Poisson noise (2016)
  9. Abdelaziz, Batoul; El Badia, Abdellatif; El Hajj, Ahmad: Direct algorithm for multipolar sources reconstruction (2015)
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  14. Matonoha, C.; Papáček, Š.: On the connection and equivalence of two methods for solving an ill-posed inverse problem based on FRAP data (2015)
  15. Mayer, Philipp A.; Packham, Natalie; Schmidt, Wolfgang M.: Static hedging under maturity mismatch (2015)
  16. Öktem, Ozan: Mathematics of electron tomography (2015)
  17. Revunova, E.G.: Analytical study of error components for solving discrete ill-posed problems using random projections (2015)
  18. Rundell, William: Recovering the density of a string from only lowest frequency data (2015)
  19. Spantini, Alessio; Solonen, Antti; Cui, Tiangang; Martin, James; Tenorio, Luis; Marzouk, Youssef: Optimal low-rank approximations of Bayesian linear inverse problems (2015)
  20. Thon, Michael; Jaeger, Herbert: Links between multiplicity automata, observable operator models and predictive state representations -- a unified learning framework (2015)

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