Dear R users,
I'm using optim to optimize a pretty complicated function. This function takes
the parameter vector "theta" and within its body I use instructions like
sigma<-theta[a:b]; computations with sigma...
out<-c()
for (i in 1:d){
a<-theta[(3*d+i):c]
out[i]<-evaluation of an expression involving 'a' (I use symbolic
differentiation)
}
Unfortunately for certain problems 'optim' returns a parameter vector which
didn't move at all from the initial parameters, and the output says that
although the function has been evaluated a high number of times, the gradient
(which I fed the function with) has been evaluated only one time. I used the
BFGS method.
By chance I looked at the help and I read "The parameter vector passed to fn
has special semantics and may be shared between calls: the function should not
change or copy it" . Could the instructions above be the cause of the failure?
If so, how to deal with symbolic differentation?
Thanks in advance,
Gildas
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