D. Commenges <Daniel.Commenges@isped.u-bordeaux2.fr>, M. Prague
<Melanie.Prague@isped.u-bordeaux2.fr> and A. Diakite
This algorithm provides a numerical solution to the
problem of minimizing a function. This is more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum. A new convergence test is
implemented (RDM) in addition to the usual stopping criterion :
stopping rule is when the gradients are small enough in the
parameters metric (GH-1G).

A Parallelized Algorithm for Least-Squares Curve Fitting
Melanie Prague, Viviane Philipps, Cecile Proust-Lima, Boris Hejblum, Daniel Commenges, Amadou Diakite
This algorithm provides a numerical solution to the
problem of minimizing (or maximizing) a function. This is more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum (or maximum). A new convergence test is
implemented (RDM) in addition to the usual stopping criterion :
stopping rule is when the gradients are small enough in the
parameters metric (GH-1G).

A Parallelized General-Purpose Optimization Based on
Marquardt-Levenberg Algorithm
Viviane Philipps, Cecile Proust-Lima, Melanie Prague, Boris Hejblum, Daniel Commenges, Amadou Diakite
This algorithm provides a numerical solution to the
problem of minimizing (or maximizing) a function. It is particularly suited for complex problems and more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives.

This algorithm provides a numerical solution to the
problem of minimizing (or maximizing) a function. It is particularly suited for complex problems and more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2020 <arXiv:2009.03840>.