A Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm
Numerical derivatives
Numerical derivatives of the gradient function
Gradient of the log-likelihood of a linear mixed model with random int...
Log-likelihood of a linear mixed model with random intercept
A parallelized general-purpose optimization based on Marquardt-Levenbe...
A parallelized general-purpose optimization based on Marquardt-Levenbe...
Summary of a marqLevAlg object
Summary of optimization
This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). 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, 2021 <doi:10.32614/RJ-2021-089>.