[Scilab-users] Some questions about Scilab Optim function

[Rafael Guerra]

Hello,

I am using Scilab 5.4 Windows7 optim function to find parameters in a
non-linear model to fit experimental data and I have some questions about
the method used in Scilab.

I am running optim with very good results despite a bit slowly, with the
following function call (x is a vector of length 3 in my case):

   [f,xopt]= optim(list(NDcost,costfun,param1,..,paramN),x0)
   ...
   function f = costfun(x,param1,...,paramN)
   ...

The questions I have regarding optim are:

1.  When using the NDcost option to compute numerically the derivatives is
optim using the quasi-Newton method without constraints?

2. Is this NDcost option equivalent in terms of run time and accuracy to
estimating numerically the derivatives with the dedicate Scilab functions
(derivative or numdiff)?

3. Can the NDcost option be run with constraints?

4. How can we have an idea of the accuracy of the parameters estimated by
optim, or at least of the stability of  the solution found?

Thanks in advance and regards,
Rafael Guerra



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