Thanks Ben for your reply,
I would very much appreciate if you could share your code. That would be great!
Sebastien
PS: As suggested, I will forward this email to John Nash.
Sébastien Bihorel <pomchip <at> free.fr> writes:
>*
*>* Dear R-users,
*>*
*>* I am trying to port to R something that I wrote in Matlab to perform model
*>* parameter optimization using the Nelder-Mead simplex method (fminsearch). I
*>* read the help on ?optim (which seems to be the way to go) as well as a bunch
*>* of posts on the topic, but I would like to make sure about something before
*>* I spend to much time trying to reproduce something that is not possible. The
*>* Matlab fminsearch has some nice features allowing the user to pass the
*>* optimization status (i.e., iteration number, objective function value,
*>* parameter estimate, algorithm porcedure,etc...) at each iteration to a
*>* custom function. In turn, this allows to save the data to file or print
*>* something to the shell.
*>*
*>* Did anybody manage to get optim similarly output the optimization status at
*>* each iteration to a function?
*>*
*>* The control=(trace=...) argument seems only to return partial data to the
*>* shell. Some post on the list also suggested to include some code inside the
*>* objective function, but this implies the execution of the code at each
*>* function evaluation rather than at each iteration.
*>*
*>* Any feedback on the topic would be appreciated.
*>*
*>* Sebastien
*>*
*
I think that what you see is more or less what you get with optim()
in this case. As I've mentioned in an earlier thread, I've translated
Nelder-Mead from _Numerical Recipes_ code into R, which would allow
you to instrument it however you wanted. (Indeed, I translated it in
order to allow me to create a picture of N-M updating that showed
which points were being visited and which updating rules executed.)
I could send it if you wanted. The output-function hook is a nice
idea, though -- might be worth mentioning to John Nash, who is working
on a project (optimx on R-forge) to extend R's optimization capabilities.
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