optim.WF: WF optimization

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

Optimization using Wolf conditions.

Usage

optim.WF(objfnc, data, start = getInitial(objfnc, data), 
control = nlr.control(tolerance = 0.001, minlanda = 1/2^10, 
maxiter = 25 * length(start)), ...)

Arguments

objfnc	any objective function for minimizing, it must contains accept formula, data and start as argument, extra argument can be passed by (...). The output of objfnc must be a list contains: $value(attr,gradient,hessian), $angmat (angular matrix),$angvec (angular vector) to check convergence. Usually it might have nl.form object as entry.
data	list of the data, that might have predictor and response variables with names.
start	list of initial values with names as parameters.
control	nlr.control options to control the optimization iterations.
...	any external parameters passe to objfnc.

Details

Optimize objective function objfnc with respect to parameters start. The mothod is gradient base using Wolf condition for rectifying the negative definit hessian problems.

The following function are called from optim.WF.

CubInrep: Cubic interpolation,

Value

result is a list of:

parameters	list of estimated parameters wit hsame names as start
objfnc	computed object function returned back by objfnc
history	history of fitt, include parameters and objective values, other level of iteration is presented for which in each iteration some more steps is done to rectify the singularity of hessian.

Note

User can define his own optimization function objfnc for any purpose, but this function designed eficiently for robust estimates. It is applied for minimizing several kind of objective functions such as heteroscedastic chi-square likelihood, robust loss, but for other general problem usage is not tested.

Together with optim.NLM are used in nlr package to acheive optimization result in failure of one another method.

This function call by nlr, for compatibility it is better to call from nlr rather than directly by user. User can use it for optimization purposes.

Author(s)

Hossein Riazoshams, May 2014. Email: [e-mail] URL http://www.riazoshams.com/nlr/

References

Nocedal J and Wright SJ 2006 Numerical optimization.. New York, NY.

See Also

nl.form, optim.NLM

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
"optim.WF"

nlr documentation built on July 31, 2019, 5:09 p.m.