[R-sig-ME] Suggestions for numerical optimization tools...
bolker at ufl.edu
Wed Apr 22 18:31:46 CEST 2009
Is optimize() really the bottleneck, or is it the computation time
in the objective function? Can you implement critical bits of the
objective function calculation in C or FORTRAN? Based on the
description in optimize(), it seems that although it's a compromise
between robustness and efficiency, that it's usually pretty efficient.
How many function evaluations are typically being required to get
to the minimum? Can you get away with a lower tolerance? Can you find
some kind of Gaussian-quadrature approximation to your integral that
makes things more efficient?
You could also try
suggested by John Nash ...
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
> Anything helps,
> [[alternative HTML version deleted]]
> R-sig-mixed-models at r-project.org mailing list
Associate professor, Biology Dep't, Univ. of Florida
bolker at ufl.edu / www.zoology.ufl.edu/bolker
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