Abstract
For calibration purposes, oftentimes various datasets are compared in such a way that observations enter the coefficient matrix of a Linear Model ("errors-in-variables"). In such a case, the Total Least-Squares approach would be appropriate that was pioneered by G. Golub and C. van Loan in the early eighties. In essence, rather than solving the usual normal equations system for the estimated parameters, the smallest singular values of a slightly extended system is set to be zero, and its eigenvector is re-scaled to provide the estimated parameter vector. The authors have recently presented their studies that show the potential of this technique to provide improved variograms for geostatistical Kriging applications.
Sometimes, however, stability or slow convergence problems may occur with the algorithm as designed so far. In order to increase the stability, additional parameters could be introduced to represent the functional model under investigation, but with a number of constraints that keep the original redundancy unchanged. In the end, the same Total Least-Squares Fit is supposed to result after fewer iterations from the newly developed scheme that, for the first time, allows the integration of constraints between the parameters, thus solving a case that was long considered "untreatable" by the original TLS algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
5. References
Felus, Y. and B. Schaffrin (2003). Fitting variogram models using the 2-stage Total Least-Squares estimator, Computers & Geosciences (submitted in April 2003).
Golub, H.G. and C. van Loan (1980). An analysis of the Total Least-Squares problem, SIAM Journal on Numerical Analysis, 17(6): 883–893.
Koch, K. R. (1999). Parameter Estimation and Hypothesis Testing in Linear Models, 2nd edition. Springer-Verlag, New York, 333 pp.
Markovsky, I., M.-L Rastello, A. Premoli, A. Kukush and S. van Huffel (2002). The element-wise weighted Total Least-Squares problem. Report No. 02-48, Dept. of Electrical Eng., Katholic University Leuven, Belgium.
Schaffrin, B. (2003): A note on Constrained Total Squares Estimation, Comput. Statistics and Data Analysis (submitted in Oct 2003).
Strang, G. (1988). Linear Algebra and its Applications, 3rd edition, Harcourt Brace Jovanovich, San Diego, 505 pp.
van Huffel, S. and J. Vandewalle (1991). The Total Least-Squares Problem. Computational Aspects and Analysis, Society for Industrial and Applied Mathematics, Philadelphia, 300 pp.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schaffrin, B., Felus, Y.A. (2005). On Total Least-Squares Adjustment with Constraints. In: Sansò, F. (eds) A Window on the Future of Geodesy. International Association of Geodesy Symposia, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27432-4_71
Download citation
DOI: https://doi.org/10.1007/3-540-27432-4_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24055-6
Online ISBN: 978-3-540-27432-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)