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Corpus ID: 221594105

A stochastic Levenberg-Marquardt method using random models with complexity results and application to data assimilation.

@article{Bergou2018ASL,
title={A stochastic Levenberg-Marquardt method using random models with complexity results and application to data assimilation.},
author={E. Bergou and Y. Diouane and V. Kungurtsev and C. Royer},
journal={arXiv: Optimization and Control},
year={2018}
}

Globally convergent variants of the Gauss-Newton algorithm are often the methods of choice to tackle nonlinear least-squares problems. Among such frameworks, Levenberg-Marquardt and trust-region methods are two well-established, similar paradigms. Both schemes have been studied when the Gauss-Newton model is replaced by a random model that is only accurate with a given probability. Trust-region schemes have also been applied to problems where the objective value is subject to noise: this… CONTINUE READING