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 189 articles , 1 standard article )

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  1. Fasino, Dario; Fazzi, Antonio: A Gauss-Newton iteration for total least squares problems (2018)
  2. Li, Hanyu; Wang, Shaoxin: On the partial condition numbers for the indefinite least squares problem (2018)
  3. Yang, Meijia; Xia, Yong; Wang, Jiulin; Peng, Jiming: Efficiently solving total least squares with Tikhonov identical regularization (2018)
  4. Diao, Huai-An; Wei, Yimin; Xie, Pengpeng: Small sample statistical condition estimation for the total least squares problem (2017)
  5. Dutta, Aritra; Li, Xin: On a problem of weighted low-rank approximation of matrices (2017)
  6. Goos, Jan; Lataire, John; Louarroudi, Ebrahim; Pintelon, Rik: Frequency domain weighted nonlinear least squares estimation of parameter-varying differential equations (2017)
  7. Volkov, V. V.; Erokhin, V. I.; Krasnikov, A. S.; Razumov, A. V.; Khvostov, M. N.: Minimum-Euclidean-norm matrix correction for a pair of dual linear programming problems (2017)
  8. Xie, Pengpeng; Xiang, Hua; Wei, Yimin: A contribution to perturbation analysis for total least squares problems (2017)
  9. Zheng, Bing; Meng, Lingsheng; Wei, Yimin: Condition numbers of the multidimensional total least squares problem (2017)
  10. Bagherpour, Negin; Mahdavi-Amiri, Nezam: A new error in variables model for solving positive definite linear system using orthogonal matrix decompositions (2016)
  11. Beck, Amir; Sabach, Shoham; Teboulle, Marc: An alternating semiproximal method for nonconvex regularized structured total least squares problems (2016)
  12. Denisov, V. I.; Timofeeva, A. Yu.; Khaĭlenko, E. A.: Estimating parameters of polynomial models with errors in variables and no additional information (2016)
  13. Gorelik, V. A.; Trembacheva (Barkalova), O. S.: Solution of the linear regression problem using matrix correction methods in the $l_1$ metric (2016)
  14. Hnětynková, Iveta; Plešinger, Martin; Sima, Diana Maria: Solvability of the core problem with multiple right-hand sides in the TLS sense (2016)
  15. Ito, Shinji; Murota, Kazuo: An algorithm for the generalized eigenvalue problem for nonsquare matrix pencils by minimal perturbation approach (2016)
  16. Jiang, Tongsong; Cheng, Xuehan; Ling, Sitao: An algebraic technique for total least squares problem in quaternionic quantum theory (2016)
  17. Nazari, Sohail; Rashidi, Bahador; Zhao, Qing; Huang, Biao: An iterative algebraic geometric approach for identification of switched ARX models with noise (2016)
  18. Pešta, Michal: Unitarily invariant errors-in-variables estimation (2016)
  19. Piotrowski, Tomasz; Yamada, Isao: Reduced-rank estimation for ill-conditioned stochastic linear model with high signal-to-noise ratio (2016)
  20. Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.: On the least-squares fitting of data by sinusoids (2016)

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