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

[Doran, Harold]

If the likelihood can be written out, why would the derivatives not be
known?  

> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org 
> [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of H c
> Sent: Wednesday, April 22, 2009 12:04 PM
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] Suggestions for numerical optimization tools...
> 
> 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
> 
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