PHILIPPS, Viviane; HEJBLUM, Boris; PRAGUE, Melanie; COMMENGES, Daniel; PROUST LIMA, Cecile

doi:10.32614/RJ-2021-089

Bordeaux population health [BPH]

HEJBLUM, Boris

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

PRAGUE, Melanie

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

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Article de revue

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R Journal. 2021-12, vol. 13, n° 2, p. 365-379

Resumen en inglés

Implementations in R of classical general-purpose algorithms for local optimization generally have two major limitations which cause difficulties in applications to complex problems: too loose convergence criteria and too long calculation time. By relying on a Marquardt-Levenberg algorithm (MLA), a Newton-like method particularly robust for solving local optimization problems, we provide with marqLevAlg package an efficient and general-purpose local optimizer which (i) prevents con vergence to saddle points by using a stringent convergence criterion based on the relative distance to minimum/maximum in addition to the stability of the parameters and of the objective function; and (ii) reduces the computation time in complex settings by allowing parallel calculations at each iteration. We demonstrate through a variety of cases from the literature that our implementation reli ably and consistently reaches the optimum (even when other optimizers fail) and also largely reduces computational time in complex settings through the example of maximum likelihood estimation of different sophisticated statistical models.< Leer menos

URI

https://oskar-bordeaux.fr/handle/20.500.12278/183236

DOI

http://dx.doi.org/10.32614/RJ-2021-089

Proyecto ANR

Modèles Dynamiques pour les Etudes Epidémiologiques Longitudinales sur les Maladies Chroniques - ANR-18-CE36-0004

Centros de investigación

Bordeaux Population Health Research Center (BPH) - UMR 1219

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Robust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg

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