nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting

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.

J. A. F. Torvisco (Facultad de Ciencias, Universidad de Extremadura) , R. Benítez (Departmento de Matemáticas para la Economía y la Empresa, Universidad de Valencia) , M. R. Arias (Facultad de Ciencias, Universidad de Extremadura) , J. Cabello Sánchez (Facultad de Ciencias, Universidad de Extremadura)

0.1 Supplementary materials

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For attribution, please cite this work as

Torvisco, et al., "nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting", The R Journal, 2023

BibTeX citation

  author = {Torvisco, J. A. F. and Benítez, R. and Arias, M. R. and Cabello Sánchez, J.},
  title = {nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting},
  journal = {The R Journal},
  year = {2023},
  note = {},
  doi = {10.32614/RJ-2023-040},
  volume = {15},
  issue = {2},
  issn = {2073-4859},
  pages = {5-24}