Poor performance of "Optim"
Have you considered the "optimx" package? I haven't tried it, but it was produced by a team of leading researchers in nonlinear optimization, including those who wrote most of "optim" (http://user2010.org/tutorials/Nash.html) years ago. There is a team actively working on this. If you could provide specific examples where Gauss and Matlab outperformed the alternatives you've tried in R, especially if Gauss and Matlab outperformed optimx, I believe they would be interested. As previously noted, nonlinear optimization is a difficult problem. An overview of alternatives available in R, including optim and optimx, is available with the CRAN Task View on optimization (http://cran.fhcrc.org/web/views/Optimization.html). Hope this helps. Spencer
On 10/1/2011 3:04 AM, Marc Girondot wrote:
Le 01/10/11 08:12, yehengxin a ?crit :
I used to consider using R and "Optim" to replace my commercial packages: Gauss and Matlab. But it turns out that "Optim" does not converge completely.
What it means "completely" ?
The same data for Gauss and Matlab are converged very well. I see that there are too many packages based on "optim" and really doubt if they can be trusted!
I don't understand the "too many". If a package needs an optimization, it is normal that it uses optim ! I use the same model in r, Excel solver (the new version is rather good) or Profit (a mac software, very powerful) and r is rather one of the best solution. But they are many different choices that can influence the optimization. You must give an example of the problem. I find some convergence problem when the criteria to be minimized is the result of a stochastic model (ie if the same set of parameters produce different objective value depending on the run). In this case the fit stops prematurely and the method SANN should be preferred. In conclusion, give us more information but take into account that non-linear optimization is a complex world ! Marc
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