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 multiplied by 10%. Comprehensive diagnostics are available by increasing the verbose level.
mxComputeNewtonRaphson(
freeSet = NA_character_,
...,
fitfunction = "fitfunction",
maxIter = 100L,
tolerance = 1e-12,
verbose = 0L
)names of matrices containing free variables
Not used. Forces remaining arguments to be specified by name.
name of the fitfunction (defaults to 'fitfunction')
maximum number of iterations
optimization is considered converged when the maximum relative change in fit is less than tolerance
integer. Level of run-time diagnostic output. Set to zero to disable
Luenberger, D. G. & Ye, Y. (2008). Linear and nonlinear programming. Springer.