deriva

The function computes the first derivates and the information score matrix.
Central finite-differences and forward finite-differences are used for the first
and second derivatives respectively.

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>.

Viviane Philipps

marqLevAlg

A Parallelized General-Purpose Optimization Based on
Marquardt-Levenberg Algorithm

deriva function

<dl><dt>nproc</dt>
<dd>number of processors for parallel computing</dd>
<dt>b</dt>
<dd>value of parameters to be optimized over</dd>
<dt>funcpa</dt>
<dd>function to be minimized (or maximized), with argument the vector
of parameters over which minimization isto take place.
It should return a scalar result.</dd>
<dt>.packages</dt>
<dd>character vector of packages that funcpa depends on</dd>
<dt>...</dt>
<dd>other arguments of the funcpa function</dd></dl>

Arguments

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

Author

Numerical derivatives — deriva

<dl>

<dt>nproc</dt>
<dd>number of processors for parallel computing</dd>


<dt>b</dt>
<dd>value of parameters to be optimized over</dd>


<dt>funcpa</dt>
<dd>function to be minimized (or maximized), with argument the vector
of parameters over which minimization isto take place.
It should return a scalar result.</dd>


<dt>.packages</dt>
<dd>character vector of packages that funcpa depends on</dd>


<dt>...</dt>
<dd>other arguments of the funcpa function</dd>

</dl>

deriva: Numerical derivatives

Description

Usage

Value

Arguments

Author

References

Examples