Hi:

A reasonable place to start would be the Optimization task view at CRAN:

http://cran.r-project.org/web/views/

HTH,
Dennis

On Tue, Aug 24, 2010 at 10:47 AM, David Beacham
<d.beacham07@imperial.ac.uk>wrote:

> I'm relatively new to R, but I'm attempting to do a non-linear maximum
> likelihood estimation (mle) in R, with the added problem that I have a
> non-linear constraint.
>
> The basic problem is linear in the parameters (a_i) and has only one
> non-linear component, b, with the problem being linear when b = 0 and
> non-linear otherwise. Furthermore, f(a_i) <= b <= g(a_i) for some (simple) f
> and g.
>
> Using optim, I can get the optimisation to work when the non-linearity is
> included but not constrained, but gives poor results (as I'd expect).
> However, I'm not sure how best to go about the constraint condition. My
> initial attempts revolve around the use of logarithmic barrier function, but
> this only appears to work when using method="CG". When using "BFGS", the
> value of b 'goes out of bounds' and the loglikelihood starts throwing NaN,
> which is particularly bad if I want to box constrain the a_i using the
> "L-BFGS-B" method.
>
> Are there any other methods/approaches/variations on the above available to
> me in the form of other packages/R functions etc? Or any good
> references/books to help me out?
>
> Any help would be greatly appreciated,
> David.
>
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