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Optimization using Wolf conditions.
optim.WF(objfnc, data, start = getInitial(objfnc, data),
control = nlr.control(tolerance = 0.001, minlanda = 1/2^10,
maxiter = 25 * length(start)), ...)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.
list of the data, that might have predictor and response variables with names.
list of initial values with names as parameters.
nlr.control options to control the optimization iterations.
any external parameters passe to objfnc.
result is a list of:
list of estimated parameters wit hsame names as start
computed object function returned back by objfnc
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.
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,
Nocedal J and Wright SJ 2006 Numerical optimization.. New York, NY.
# NOT RUN {
##---- 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"
# }
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