deriva_grad: Numerical derivatives of the gradient function

In marqLevAlg: A Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm

Numerical derivatives of the gradient function

Description

The function computes the information score matrix in the case where the first derivatives of the function to optimize are analytically known. Therefore, minus the derivatives of the gradient are computed by central finite differences.

Usage

deriva_grad(nproc = 1, b, grad, .packages = NULL, ...)

Arguments

nproc	number of processors for parallel computing
b	value of parameters to be optimized over
grad	the gradient of the function to be minimized (or maximized)
.packages	character vector of packages that grad depends on
...	other arguments of the grad function

Value

hessian

vector containing the upper part of the information score matrix

Author(s)

Viviane Philipps, Boris Hejblum, Cecile Proust-Lima, Daniel Commenges

marqLevAlg documentation built on March 31, 2023, 6:33 p.m.