An algorithm for least squares analysis of spectroscopic data

DOI10.1007/BF01931260zbMath0418.65007OpenAlexW2003847041MaRDI QIDQ3851504

A. K. Rigler, D. C. st. Clair

Publication date: 1979

Published in: BIT Numerical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01931260

zbMATH Keywords

orthogonalizationcurve fittingleast squares analysisanalysis of spectroscopic datamodified Gauss-Newton algorithm

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Numerical solutions to overdetermined systems, pseudoinverses (65F20) General nonlinear regression (62J02) Computing methodologies and applications (68U99) Physical optics (78A10)

Related Items (1)

Curve fitting and identification of physical spectra

Cites Work

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• The Modified Gauss-Newton Method for the Fitting of Non-Linear Regression Functions by Least Squares
• `` Direct Search Solution of Numerical and Statistical Problems
• A variable projection method for solving separable nonlinear least squares problems
• Curve fitting by use of graphic display
• A Method for Separable Nonlinear Least Squares Problems with Separable Nonlinear Equality Constraints
• Numerically Stable Methods for Updating Regressions
• Experiments on Gram-Schmidt Orthogonalization
• An Algorithm for Least Squares Estimation of Nonlinear Parameters When Some of the Parameters Are Linear
• The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate
• Inversion of Triple-Diagonal Compound Matrices

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