Optimization in R

Hi,
In my earlier post I eluded to a situation where that would be useful. 
In nlme, there is a choice of optimizers, minpack.lm has
Levenberg-Marquardt,
while nlminb has the port routines. For the same starting values,
different
optimizers will present different solutions, having a common interface
would make fitting with multiple optimizers very attractive. 

Also the inverse Hessian would be useful, for cases where the Hessian is
ill conditioned
a little regularization goes a long way. I believe the package accuracy 
has a nice solution.

Nicholas

For the same reason that generic functions exist.  They don't have
a lot of common code but it makes easier to use.  Perhaps the argument
is not as strong here since the class tends to be implicit whereas the
method is explicit but it would still be a convenience.

Can you give other examples where we do this?  The ones I can think of 
(graphics drivers and finalizers) involve a large amount of common 
machinery that it's difficult or impossible for the user to duplicate. 
That's not the case here.

Duncan Murdoch