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 222 articles , 1 standard article )

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  1. Han, Feiyang; Wei, Yimin: TLS-EM algorithm of mixture density models for exponential families (2022)
  2. Liu, Qiaohua; Jia, Zhigang; Wei, Yimin: Multidimensional total least squares problem with linear equality constraints (2022)
  3. Contino, Maximiliano; Fongi, Guillermina; Maestripieri, Alejandra; Muro, Santiago: Total least squares problems on infinite dimensional spaces (2021)
  4. Liu, Qiaohua; Chen, Cuiping; Zhang, Qian: Perturbation analysis for total least squares problems with linear equality constraint (2021)
  5. Sugakova, Olena; Maiboroda, Rostyslav: Principal components analysis for mixtures with varying concentrations (2021)
  6. Almekkawy, Mohamed; Carević, Anita; Abdou, Ahmed; He, Jiayu; Lee, Geunseop; Barlow, Jesse: Regularization in ultrasound tomography using projection-based regularized total least squares (2020)
  7. Hallman, Eric: Estimating the backward error for the least-squares problem with multiple right-hand sides (2020)
  8. Hladík, Milan; Černý, Michal; Antoch, Jaromír: EIV regression with bounded errors in data: total `least squares’ with Chebyshev norm (2020)
  9. Liu, Qiaohua; Jin, Shufang; Yao, Lei; Shen, Dongmei: The revisited total least squares problems with linear equality constraint (2020)
  10. Liu, Qiaohua; Wei, Musheng; Chen, Cuiping: A note on the matrix-scaled total least squares problems with multiple solutions (2020)
  11. Luiken, Nick; van Leeuwen, Tristan: Seismic wavefield redatuming with regularized multi-dimensional deconvolution (2020)
  12. Meng, Lingsheng; Zheng, Bing; Wei, Yimin: Condition numbers of the multidimensional total least squares problems having more than one solution (2020)
  13. 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)
  14. Song, Juan: USSOR method for solving the indefinite least squares problem (2020)
  15. Zhang, Liping; Wei, Yimin: Randomized core reduction for discrete ill-posed problem (2020)
  16. Diao, Huai-An; Zhou, Tong-Yu: Backward error and condition number analysis for the indefinite linear least squares problem (2019)
  17. Hnětynková, Iveta; Plešinger, Martin; Žáková, Jana: Solvability classes for core problems in matrix total least squares minimization. (2019)
  18. Hnětynková, Iveta; Plešinger, Martin; Žáková, Jana: On TLS formulation and core reduction for data fitting with generalized models (2019)
  19. Wang, Hefeng; Xiang, Hua: Quantum algorithm for total least squares data fitting (2019)
  20. Xia, Yong; Wang, Longfei; Yang, Meijia: A fast algorithm for globally solving Tikhonov regularized total least squares problem (2019)

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