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Least-Squares Fitting Algorithms of the NIST Algorithm Testing System

Published

Author(s)

Craig M. Shakarji

Abstract

This report describes algorithms for fitting certain curves and surfaces to points in three dimensions. All fits are based on orthogonal distance regression. The algorithms were developed as reference software for the NIST Algorithm Testing System, which has been used for five years by NIST and by members of the ASME B89.4.10[3]. The Algorithm Testing System itself is described only briefly; the main part of this paper covers the general linear algebra, numerical analysis, and optimization methods it employs. Most of the fitting routines rely on the Levenberg-Marquardt optimization routine.
Citation
Journal of Research of the National Institute of Standards and Technology
Volume
103

Keywords

coordinate measuring machine, curve fitting, least-squares fitting, Levenberg-Marquardt, orthogonal distance regression, surface fitting

Citation

Shakarji, C. (1998), Least-Squares Fitting Algorithms of the NIST Algorithm Testing System, Journal of Research of the National Institute of Standards and Technology, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821955 (Accessed July 21, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created December 1, 1998, Updated February 17, 2017