Title: An algorithm for least-squares curve fitting
Author: D. Commenges <Daniel.Commenges@isped.u-bordeaux2.fr>, M. Prague
Description: 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 vey
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

the Gauss-Newton-like algorithm when starting from points very

Title: A Parallelized Algorithm for Least-Squares Curve Fitting
Author: Melanie Prague, Viviane Philipps, Cecile Proust-Lima, Boris Hejblum, Daniel Commenges, Amadou Diakite
problem of minimizing (or maximizing) a function. This is more efficient than
far from the final minimum (or maximum). A new convergence test is

Title: A Parallelized General-Purpose Optimization Based on
Author: Viviane Philipps, Cecile Proust-Lima, Melanie Prague, Boris Hejblum, Daniel Commenges, Amadou Diakite
problem of minimizing (or maximizing) a function. It is particularly suited for complex problems and more efficient than
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.

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>.

problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than

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, 2021 <doi:10.32614/RJ-2021-089>.

Authors@R: c(person("Alencar", "Xavier", role=c("aut", "cre"), email = "alenxav@gmail.com"),person("Shizhong", "Xu", role=c("aut", "ctb")))
Description: Genome-wide association analysis with membership information, variance adjustment, and correlated responses.