scholarly journals Roundoff Error Analysis of an Algorithm Based on Householder Bidiagonalization for Total Least Squares Problems

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2550
Author(s):  
Zhanshan Yang ◽  
Xilan Liu

For large-scale problems, how to establish an algorithm with high accuracy and stability is particularly important. In this paper, the Householder bidiagonalization total least squares (HBITLS) algorithm and nonlinear iterative partial least squares for total least squares (NIPALS-TLS) algorithm were established, by which the same approximate TLS solutions was obtained. In addition, the propagation of the roundoff error for the process of the HBITLS algorithm was analyzed, and the mixed forward-backward stability of these two algorithms was proved. Furthermore, an upper bound of roundoff error was derived, which presents a more detailed and clearer approximation of the computed solution.

2000 ◽  
Vol 22 (2) ◽  
pp. 413-429 ◽  
Author(s):  
Åke Björck ◽  
P. Heggernes ◽  
P. Matstoms

1993 ◽  
Vol 04 (01) ◽  
pp. 55-68 ◽  
Author(s):  
MARC MOONEN

Total least squares parameter estimation is an alternative to least squares estimation though much less used in practice, partly due to the absence of efficient recursive algorithms or parallel architectures. Here it is shown how previously developed systolic algorithms/architectures for recursive least squares estimation can be used for recursive total least squares problems. Unconstrained as well as linearly constrained and "mixed RLS/RTLS" problems are considered.


2020 ◽  
Vol 48 (4) ◽  
pp. 987-1003
Author(s):  
Hans Georg Bock ◽  
Jürgen Gutekunst ◽  
Andreas Potschka ◽  
María Elena Suaréz Garcés

AbstractJust as the damped Newton method for the numerical solution of nonlinear algebraic problems can be interpreted as a forward Euler timestepping on the Newton flow equations, the damped Gauß–Newton method for nonlinear least squares problems is equivalent to forward Euler timestepping on the corresponding Gauß–Newton flow equations. We highlight the advantages of the Gauß–Newton flow and the Gauß–Newton method from a statistical and a numerical perspective in comparison with the Newton method, steepest descent, and the Levenberg–Marquardt method, which are respectively equivalent to Newton flow forward Euler, gradient flow forward Euler, and gradient flow backward Euler. We finally show an unconditional descent property for a generalized Gauß–Newton flow, which is linked to Krylov–Gauß–Newton methods for large-scale nonlinear least squares problems. We provide numerical results for large-scale problems: An academic generalized Rosenbrock function and a real-world bundle adjustment problem from 3D reconstruction based on 2D images.


1989 ◽  
Vol 26 (3) ◽  
pp. 637-660 ◽  
Author(s):  
E. P. Papadopoulou ◽  
Y. G. Saridakis ◽  
T. S. Papatheodorou

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