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A derivative-free Levenberg–Marquardt method for sparse nonlinear least squares problems

Yuchen Feng et al.

Computational Optimization and Applications2026https://doi.org/10.1007/s10589-026-00784-2article
AJG 3
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0.50

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https://doi.org/https://doi.org/10.1007/s10589-026-00784-2

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@article{yuchen2026,
  title        = {{A derivative-free Levenberg–Marquardt method for sparse nonlinear least squares problems}},
  author       = {Yuchen Feng et al.},
  journal      = {Computational Optimization and Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10589-026-00784-2},
}

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A derivative-free Levenberg–Marquardt method for sparse nonlinear least squares problems

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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