[R] Poor performance of "Optim"

[Spencer Graves]

       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 écrit :
>> 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


-- 
Spencer Graves, PE, PhD
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