2018
DOI: 10.1007/s00211-018-0977-z
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A Levenberg–Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients

Abstract: This is an author's version published in: http://oatao.univ-toulouse.fr/24842To cite this version: Bellavia, Stefania and Gratton, Serge and Riccietti, Elisa A Levenberg-Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients. (2018) Numerische Mathematik, 140 (3). 791-825. ISSN 0029-599X Official URL: https://doi. AbstractIn this paper we consider large scale nonlinear least-squares problems for which function and gradient are evaluated with dynamic accurac… Show more

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Cited by 49 publications
(38 citation statements)
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“…When both the model operator and the gradient are not available exactly, inexact methods need to be used. In [18,23] a Levenberg-Marquardt method is proposed and investigated for dealing with inexact gradients and Jacobians.…”
Section: Hybrid Methods and Inexact Approachesmentioning
confidence: 99%
See 1 more Smart Citation
“…When both the model operator and the gradient are not available exactly, inexact methods need to be used. In [18,23] a Levenberg-Marquardt method is proposed and investigated for dealing with inexact gradients and Jacobians.…”
Section: Hybrid Methods and Inexact Approachesmentioning
confidence: 99%
“…For nonlinear problems, ∇ 2 J i (x i ) −1 is an approximation to the posterior covariance. Solving (18) iteratively is more advantageous than computing the Kalman gain directly for very large problems with sparsity structure. More details on this idea can be found in [13].…”
Section: Compute the Forecast Error Covariancementioning
confidence: 99%
“…In our setting, the Levenberg-Marquardt algorithm is used to update the parameter vector ψ iteratively by solving the nonlinear optimization problem described in (15). The algorithm adaptively updates the parameter estimates by combining the gradient descent update and the Gauss-Newton update [27] by tuning a damping parameter λ. The Marquardt's update equation is given by:…”
Section: Parameter Estimationmentioning
confidence: 99%
“…The damping factor λ is adjusted by checking the values obtained with the new parameter set against the previous values. One possible way to do this is by using a ρ factor [27,29,30] defined in (19).…”
Section: Parameter Estimationmentioning
confidence: 99%
“…e effectiveness of the algorithm was verified by numerical examples. Bellavia et al [31] improved the approximation function by controlling the accuracy level when the accuracy was too low to be optimized, and then proposed the LM method based on the dynamic precision relationship between the evaluation function and gradient for solving large-scale nonlinear least squares problems. ey proved the global and local convergence and complexity of this method.…”
Section: Introductionmentioning
confidence: 99%