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Poor performance of "Optim"

2 messages · Marc Girondot, Spencer Graves

Marc Girondot
# 
Le 01/10/11 08:12, yehengxin a ?crit :
What it means "completely" ?
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
# 
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: