R help - Oct 2004 - constrained optimization using nlm/optim?

I'm looking for an example of a simple R script that impliments a
contrained nonlinear function using nlm or optim. I'm not exactly sure how
to impliment the constraints within the objective function that is passed
to nlm/optim.

obj.func <- function( p ) {

   x(p) <- unconstrained obj function value

   if( constraint1 > something ) {
      obj.func <- x(p)
   } else {
      obj.func <- some super huge number
  }

}

p <- c(0.1,2.4, 5)

nlm( obj.func, p, and a bunch of other arguments )

Any suggestions would be very helpful...

Jeff.



-- 
Jeff D. Hamann
Forest Informatics, Inc.
PO Box 1421
Corvallis, Oregon 97339-1421
phone 541-754-1428
fax 541-752-0288
jeff.hamann at forestinformatics.com
http://www.forestinformatics.com

You want the penalty for the constraint to depend on how
much the constraint is broken -- unlike your sample code.
Page 336 of S Poetry has an example.

Patrick Burns

Burns Statistics
patrick at burns-stat.com
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")

Jeff D. Hamann wrote:
>I'm looking for an example of a simple R script that impliments a
>contrained nonlinear function using nlm or optim. I'm not exactly sure
how
>to impliment the constraints within the objective function that is passed
>to nlm/optim.
>
>obj.func <- function( p ) {
>
>   x(p) <- unconstrained obj function value
>
>   if( constraint1 > something ) {
>      obj.func <- x(p)
>   } else {
>      obj.func <- some super huge number
>  }
>
>}
>
>p <- c(0.1,2.4, 5)
>
>nlm( obj.func, p, and a bunch of other arguments )
>
>Any suggestions would be very helpful...
>
>Jeff.
>
>
>
>  
>

My guess is that approach will not work particularly well with nlm(), 
which expects the objective function to be smooth.  You might have 
better luck using the Nelder-Mead method in optim().

-roger

Jeff D. Hamann wrote:
> I'm looking for an example of a simple R script that impliments a
> contrained nonlinear function using nlm or optim. I'm not exactly sure
how
> to impliment the constraints within the objective function that is passed
> to nlm/optim.
> 
> obj.func <- function( p ) {
> 
>    x(p) <- unconstrained obj function value
> 
>    if( constraint1 > something ) {
>       obj.func <- x(p)
>    } else {
>       obj.func <- some super huge number
>   }
> 
> }
> 
> p <- c(0.1,2.4, 5)
> 
> nlm( obj.func, p, and a bunch of other arguments )
> 
> Any suggestions would be very helpful...
> 
> Jeff.
> 
> 
>