The total least squares problem: computational aspects and analysis. Total least squares (TLS) is one of the several linear parameter estimation techniques that have been devised to compensate for data errors. It is also known as the errors-in-variables model. The renewed interest in the TLS method is mainly due to the development of computationally efficient and numerically reliable TLS algorithms. Much attention is paid in this book to the computational aspects of TLS and new algorithms are presented. .. (netlib vanhuffel) (Source:

References in zbMATH (referenced in 163 articles , 1 standard article )

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  1. Diao, Huai-An; Wei, Yimin; Xie, Pengpeng: Small sample statistical condition estimation for the total least squares problem (2017)
  2. Dutta, Aritra; Li, Xin: On a problem of weighted low-rank approximation of matrices (2017)
  3. Xie, Pengpeng; Xiang, Hua; Wei, Yimin: A contribution to perturbation analysis for total least squares problems (2017)
  4. Bagherpour, Negin; Mahdavi-Amiri, Nezam: A new error in variables model for solving positive definite linear system using orthogonal matrix decompositions (2016)
  5. Beck, Amir; Sabach, Shoham; Teboulle, Marc: An alternating semiproximal method for nonconvex regularized structured total least squares problems (2016)
  6. Gorelik, V.A.; Trembacheva, O.S.: Solution of the linear regression problem using matrix correction methods in the $l_1$ metric (2016)
  7. Hnětynková, Iveta; Plešinger, Martin; Sima, Diana Maria: Solvability of the core problem with multiple right-hand sides in the TLS sense (2016)
  8. Ito, Shinji; Murota, Kazuo: An algorithm for the generalized eigenvalue problem for nonsquare matrix pencils by minimal perturbation approach (2016)
  9. Jiang, Tongsong; Cheng, Xuehan; Ling, Sitao: An algebraic technique for total least squares problem in quaternionic quantum theory (2016)
  10. Pešta, Michal: Unitarily invariant errors-in-variables estimation (2016)
  11. Piotrowski, Tomasz; Yamada, Isao: Reduced-rank estimation for ill-conditioned stochastic linear model with high signal-to-noise ratio (2016)
  12. Briot, Sébastien; Gautier, Maxime: Global identification of joint drive gains and dynamic parameters of parallel robots (2015)
  13. So, C.F.; Leung, S.H.: Maximum likelihood whitening pre-filtered total least squares for resolving closely spaced signals (2015) ioport
  14. Li, Hanyu; Wang, Shaoxin; Yang, Hu: On mixed and componentwise condition numbers for indefinite least squares problem (2014)
  15. Mastronardi, Nicola; Van Dooren, Paul: An algorithm for solving the indefinite least squares problem with equality constraints (2014)
  16. Signoretto, Marco; Dinh, Quoc Tran; De Lathauwer, Lieven; Suykens, Johan A.K.: Learning with tensors: a framework based on convex optimization and spectral regularization (2014)
  17. Tran, Quoc Huy; Chin, Tat-Jun; Chojnacki, Wojciech; Suter, David: Sampling minimal subsets with large spans for robust estimation (2014)
  18. Zhang, Songlin; Zhang, Kun: On a basic multivariate EIV model with linear equality constraints (2014)
  19. Berthoumieux, Sara; Brilli, Matteo; Kahn, Daniel; de Jong, Hidde; Cinquemani, Eugenio: On the identifiability of metabolic network models (2013)
  20. Clotet, Josep; Magret, M.Dolors: Upper bounds for the distance between a controllable switched linear system and the set of uncontrollable ones (2013)

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