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Hi<br>
<br>
The objective function should be a real number, I believe. <br>
If this si true, it should not have the same size than the data
vector (it should be 1x1 real number)<br>
<br>
May be you could use a norm of the cost function you are currently
using. Do that using scilab function norm.<br>
<br>
Hope it helps<br>
<br>
On 17/08/2011 17:09, Peter Hinow wrote:
<blockquote
cite="mid:1274391701.23522.1313617158901.JavaMail.root@mail06.pantherlink.uwm.edu"
type="cite">
<pre wrap="">Dear fellows,
is there a "canonical" way to fit the solution of a parameter-dependent ordinary differential equation y' = f(y;p) to some experimental data?
I have tried leastsq, but the optimal solution is always the initial guess, regardless of the initial guess. Specifically, I'm not able to get any information why leastsq terminates, even when I add the option "imp=2" (the "optimal" gradient has an entry of order 10^21). When I try datafit instead, I get an error like
Submatrix incorrectly defined.
at line 26 of function costf called by :
at line 174 of function datafit called by :
even though the output of the objective function has the same size as the data vector.
Thank you very much for your help,
Peter
</pre>
</blockquote>
<br>
<br>
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