nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting

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Bibliographic Details
Main Authors:	Torvisco, J. A. F., Benítez, R., Arias, M. R., Sánchez, J. Cabello
Format:	Preprint
Published:	2024
Subjects:	Statistics Theory
Online Access:	Acceder al recurso
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author	Torvisco, J. A. F. Benítez, R. Arias, M. R. Sánchez, J. Cabello
author_facet	Torvisco, J. A. F. Benítez, R. Arias, M. R. Sánchez, J. Cabello
contents	A new package for nonlinear least squares fitting is introduced in this paper. This package implements a recently developed algorithm that, for certain types of nonlinear curve fitting, reduces the number of nonlinear parameters to be fitted. One notable feature of this method is the absence of initialization which is typically necessary for nonlinear fitting gradient-based algorithms. Instead, just some bounds for the nonlinear parameters are required. Even though convergence for this method is guaranteed for exponential decay using the max-norm, the algorithm exhibits remarkable robustness, and its use has been extended to a wide range of functions using the Euclidean norm. Furthermore, this data-fitting package can also serve as a valuable resource for providing accurate initial parameters to other algorithms that rely on them.
format	Preprint
id	arxiv_https___arxiv_org_abs_2402_04124
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting Torvisco, J. A. F. Benítez, R. Arias, M. R. Sánchez, J. Cabello Statistics Theory A new package for nonlinear least squares fitting is introduced in this paper. This package implements a recently developed algorithm that, for certain types of nonlinear curve fitting, reduces the number of nonlinear parameters to be fitted. One notable feature of this method is the absence of initialization which is typically necessary for nonlinear fitting gradient-based algorithms. Instead, just some bounds for the nonlinear parameters are required. Even though convergence for this method is guaranteed for exponential decay using the max-norm, the algorithm exhibits remarkable robustness, and its use has been extended to a wide range of functions using the Euclidean norm. Furthermore, this data-fitting package can also serve as a valuable resource for providing accurate initial parameters to other algorithms that rely on them.
title	nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting
topic	Statistics Theory
url	https://arxiv.org/abs/2402.04124

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