mxComputeNewtonRaphson: Optimize parameters using the Newton-Raphson algorithm
Description
This optimizer requires analytic 1st and 2nd derivatives of the
fit function. Box constraints are supported. Parameters can
approach box constraints but will not leave the feasible region
(even by some small epsilon>0). Non-finite fit values are
interpreted as soft feasibility constraints. That is, when a
non-finite fit is encountered, line search is continued after the
step size is multipled by 10
available by increasing the verbose level.Usage
mxComputeNewtonRaphson(freeSet = NA_character_, ...,
fitfunction = "fitfunction", maxIter = 100L, tolerance = 1e-12,
verbose = 0L)
Arguments
freeSet
names of matrices containing free variables
...
Not used. Forces remaining arguments to be specified by name.
fitfunction
name of the fitfunction (defaults to 'fitfunction')
maxIter
maximum number of iterations
tolerance
optimization is considered converged when the maximum relative change in fit is less than tolerance
verbose
level of debugging output
References
Luenberger, D. G. & Ye, Y. (2008). Linear and nonlinear programming. Springer.