The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Least-squares fitting algorithms of the NIST algorithm testing system
Open accessAbstract
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 National Institute of Standards and Technology's Algorithm Testing System, which has been used for 5 years by NIST and by members of the American Society of Mechanical Engineers' B89.4.10 standards committee. 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.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
The Levenberg-Marquardt algorithm: Implementation and theory
Moré, J. J. (1978) · Lecture Notes in Mathematics · doi:10.1007/bfb0067700 · Cited by 5,773
Compact Numerical Methods for Computers: Linear Algebra and Function Minimisation
Nash, J. C. (1990) · Cited by 392
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HELFIT: Helix fitting by a total least squares method
Enkhbayar, P., Damdinsuren, S., Osaki, M., & Matsushima, N. (2008) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2008.03.012 · Cited by 58
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Hauser, D., Glennie, C., & Brooks, B. (2016) · Journal of Surveying Engineering · doi:10.1061/(asce)su.1943-5428.0000178 · Cited by 29
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Metadata
- Authors
- Shakarji, C. M.
- Venue
- Journal of Research of the National Institute of Standards and Technology
- Published
- 1998-11
- Volume
- 103
- Issue
- 6
- Pages
- 633
- ISSN
- 1044-677X (ISSN-L)
- OpenAlex
- W2319895531
- Cited by
- 355
- References
- 3
- FWCI
- 1.42
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
