Abstract
In atmospheric remote sensing, near real-time software processors frequently use approximations of the Jacobian matrix in order to speed up the calculation. If the forward model F(x) depends on the state vector x through some model parameters bk, F(x) = F(b1 (x),..., bN (x)),
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
- Iteration Step
- Tikhonov Regularization
- Total Little Square
- Minimum Norm Solution
- Quadratic Eigenvalue Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Golub, G. H., Hansen P. C., and O’Leary, D. P. (1999). Tikhonov regularization and total least squares. SIAM J. Matrix Anal. Appl. 21, 185—194.
Renault, R. A. and Guo, H. (2005). Efficient algorithms for solution of regularized total least squares. SIAM J. Matrix Anal. Appl. 26, 457—476.
Sima, D., Van Huffel, S., and Golub, G. H. (2003). Regularized Total Least Squares Based on Quadratic Eigenvalue Problem Solvers. Technical Report SCCM—03—03, Stanford University, Stanford, CA.
Van Huffel, S. and Vanderwalle, J. (1991). The Total Least Squares Problem: Computa- tional Aspects and Analysis. SIAM, Philadelphia, PA.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Doicu, A., Trautmann, T., Schreier, F. (2010). Total least squares. In: Numerical Regularization for Atmospheric Inverse Problems. Springer Praxis Books(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05439-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-05439-6_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05438-9
Online ISBN: 978-3-642-05439-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)