Abstract
The Levenberg–Marquardt method is widely used for solving nonlinear systems of equations, as well as nonlinear least-squares problems. In this paper, we consider local convergence properties of the method, when applied to nonzero-residue nonlinear least-squares problems under an error bound condition, which is weaker than requiring full rank of the Jacobian in a neighborhood of a stationary point. Differently from the zero-residue case, the choice of the Levenberg–Marquardt parameter is shown to be dictated by (i) the behavior of the rank of the Jacobian and (ii) a combined measure of nonlinearity and residue size in a neighborhood of the set of (possibly non-isolated) stationary points of the sum of squares function.
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Acknowledgements
We are thankful to two anonymous referees, whose suggestions helped to improve the first version of this paper. This work was partially supported by the Brazilian research agencies CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo): R. Behling Grants 304392/2018-9 and 429915/2018-7, D. S. Gonçalves Grant 421386/2016-9, S. A. Santos Grants 302915/2016-8, 2018/24293-0 and 2013/07375-0. The first author wants to thank the Federal University of Santa Catarina and remarks that his contribution to the present article was predominantly carried out at this institution.
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Communicated by Nobuo Yamashita.
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Behling, R., Gonçalves, D.S. & Santos, S.A. Local Convergence Analysis of the Levenberg–Marquardt Framework for Nonzero-Residue Nonlinear Least-Squares Problems Under an Error Bound Condition. J Optim Theory Appl 183, 1099–1122 (2019). https://doi.org/10.1007/s10957-019-01586-9
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DOI: https://doi.org/10.1007/s10957-019-01586-9