VanHuffel

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: http://plato.asu.edu)


References in zbMATH (referenced in 211 articles , 1 standard article )

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  1. Hallman, Eric: Estimating the backward error for the least-squares problem with multiple right-hand sides (2020)
  2. Hladík, Milan; Černý, Michal; Antoch, Jaromír: EIV regression with bounded errors in data: total `least squares’ with Chebyshev norm (2020)
  3. Liu, Qiaohua; Jin, Shufang; Yao, Lei; Shen, Dongmei: The revisited total least squares problems with linear equality constraint (2020)
  4. Liu, Qiaohua; Wei, Musheng; Chen, Cuiping: A note on the matrix-scaled total least squares problems with multiple solutions (2020)
  5. Meng, Lingsheng; Zheng, Bing; Wei, Yimin: Condition numbers of the multidimensional total least squares problems having more than one solution (2020)
  6. Quintana Carapia, Gustavo; Markovsky, Ivan; Pintelon, Rik; Csurcsia, Péter Zoltán; Verbeke, Dieter: Bias and covariance of the least squares estimate in a structured errors-in-variables problem (2020)
  7. Zhang, Liping; Wei, Yimin: Randomized core reduction for discrete ill-posed problem (2020)
  8. Hnětynková, Iveta; Plešinger, Martin; Žáková, Jana: Solvability classes for core problems in matrix total least squares minimization. (2019)
  9. Hnětynková, Iveta; Plešinger, Martin; Žáková, Jana: On TLS formulation and core reduction for data fitting with generalized models (2019)
  10. Xia, Yong; Wang, Longfei; Yang, Meijia: A fast algorithm for globally solving Tikhonov regularized total least squares problem (2019)
  11. Xu, Peiliang: Improving the weighted least squares estimation of parameters in errors-in-variables models (2019)
  12. Blanco, Víctor; Puerto, Justo; Salmerón, Román: Locating hyperplanes to fitting set of points: a general framework (2018)
  13. Fasino, Dario; Fazzi, Antonio: A Gauss-Newton iteration for total least squares problems (2018)
  14. Li, Hanyu; Wang, Shaoxin: On the partial condition numbers for the indefinite least squares problem (2018)
  15. Salahi, M.; Taati, A.: An efficient algorithm for solving the generalized trust region subproblem (2018)
  16. Shklyar, Sergiy: Consistency of the total least squares estimator in the linear errors-in-variables regression (2018)
  17. Yang, Meijia; Xia, Yong; Wang, Jiulin; Peng, Jiming: Efficiently solving total least squares with Tikhonov identical regularization (2018)
  18. Diao, Huai-An; Wei, Yimin; Xie, Pengpeng: Small sample statistical condition estimation for the total least squares problem (2017)
  19. Dutta, Aritra; Li, Xin: On a problem of weighted low-rank approximation of matrices (2017)
  20. Goos, Jan; Lataire, John; Louarroudi, Ebrahim; Pintelon, Rik: Frequency domain weighted nonlinear least squares estimation of parameter-varying differential equations (2017)

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