[R-sig-ME] Suggestions for numerical optimization tools...

Ken Beath ken at kjbeath.com.au
Wed Apr 22 23:37:00 CEST 2009

I assume this is part of the mixture model that you have mentioned  
previously, where you used EM.

It is probably better to use GEM, that is not to allow the  
optimisation of the maximisation part to complete but perform only a  
small number of iterations, possibly even 1. When the EM looks close  
to convergence switching to optimising the complete likelihood can  
also help.


On 23/04/2009, at 2:04 AM, H c wrote:

> My current program relies on the ML estimation of a parameter,  
> \phi,  based
> on numerical methods.  The parameter, \phi,  lies on the 0 to 1  
> interval and
> evaluation of the ML given any value of \phi is computationally  
> expensive.
> I am currently using the "optimize()" function, optimizing the  
> Likelihood
> with respect to phi.  This is extremely computationally expensive  
> and is the
> bottleneck of an otherwise efficient program.
> Does anyone have any suggestions of better tools for numerical  
> optimization.
> (Derivatives are not known, so gradient decent options do not appear  
> to be
> applicable).
> Anything helps,
> Harlan
> 	[[alternative HTML version deleted]]
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

More information about the R-sig-mixed-models mailing list