[Scilab-users] Some questions about Scilab Optim function

[Serge Steer]

----- Mail original -----
> De: "Rafael Guerra" <jrafaelbguerra at hotmail.com>
> À: users at lists.scilab.org
> Envoyé: Lundi 21 Janvier 2013 14:33:03
> Objet: [Scilab-users] Some questions about Scilab Optim function
> 
> 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?

The NDcost functio is just a way to give optim a function able to compute the cost and/or the gradient. So all optim options are available in this context. The quality and efficiency will depend on the quality of the estimated derivative

> 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)?
Yes
> 
> 3. Can the NDcost option be run with constraints?
> 
Yes, under the assumption the numericall derivative is precise enough

> 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?

You can look at the gradient or projected gradient norm


Serge Steer