Levenberg-Marquardt methods based on probabilistic gradient models and inexact subproblem solution, with application to data assimilation - Archive ouverte HAL Access content directly
Journal Articles SIAM/ASA Journal on Uncertainty Quantification Year : 2016

Levenberg-Marquardt methods based on probabilistic gradient models and inexact subproblem solution, with application to data assimilation

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Abstract

The Levenberg-Marquardt algorithm is one of the most popular algorithms for the solution of nonlinear least squares problems. Motivated by the problem structure in data assimilation, we consider in this paper the extension of the classical Levenberg--Marquardt algorithm to the scenarios where the linearized least squares subproblems are solved inexactly and/or the gradient model is noisy and accurate only within a certain probability. Under appropriate assumptions, we show that the modified algorithm converges globally to a first order stationary point with probability one. Our proposed approach is first tested on simple problems where the exact gradient is perturbed with a Gaussian noise or only called with a certain probability. It is then applied to an instance in variational data assimilation where stochastic models of the gradient are computed by the so-called ensemble methods.
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Dates and versions

hal-02147989 , version 1 (05-06-2019)

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El Houcine Bergou, Serge Gratton, Luis Vicente. Levenberg-Marquardt methods based on probabilistic gradient models and inexact subproblem solution, with application to data assimilation. SIAM/ASA Journal on Uncertainty Quantification, 2016, 4 (1), pp.924-951. ⟨10.1137/140974687⟩. ⟨hal-02147989⟩
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