Academic question

[Carrico, Paul]

Dear All,

 

For some times I'm testing Scilab macros for optimization purposes ;
"optimization" has to be intended here as an inverse method for fitting
parameters (i.e. so that finite element analysis fit test measurements).

 

I tested basic macros such as fminsearch for unbounded fitting as well
as Nelder-Mead one for bracketed parameters fitting. Basically the "cost
function" is the normalized Sum of the Square errors SSE from the FEA to
the measurements.

 

In my FEA's the variables can be material parameters, spring
stiffness's, damping ratio and so on : the simplex method is well
adapted since the former variables are not analytically described in the
cost function and since it's not necessary to calculate the gradient
vector nor the Hessian matrix ...

 

 

If this method is robust nevertheless it remains rather slow !

 

 

Is there a way or another macro I can use to reduce the number of loops
and the time consuming consequently ?

 

Nota : I was thinking in OPTIM macro but from my understanding the cost
function need to be twice differentiable at least  (for the Gradient and
the Hessian ) i.e. analytically link to the parameters ... isn't it ?

 

Thanks in advance for any advice (for a better understanding)

 

Paul

 

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