On 20/09/2012 09:24, Gildas Mazo wrote:
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.
On face value that means it is unable to find a small step that goes downhill consistent with the gradient, and usually indicates an error in the gradient function or using numerical derivatives on a non-differentiable function.
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?
None of the code you show us changes 'theta'. It would be a very unusual thing to do, but has happened in error when people have used compiled code.
Thanks in advance, Gildas
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