Wed Jun 26 11:26:10 CDT 2013
I am experimenting the optimize module of scipy.
My optimization problem is a leastsq problem.
However, the leastsq function seems to be not appropriate for two reasons:
- there is no possibility to specify a covariance matrix between the
leastsq terms. They are supposed to be independent, which is a too strong
assumption in my case.
- the analyzed covariance matrix (i.e. the inverse of the jacobian of the
cost function) cannot be simply outputed.
Of course I could use a more generic optimization function, like the
However this seems sub-optimal because the minimisation of a least squares
problem can dealt more efficiently (the jacobian of the cost function can
be approximated using the jacobian of the terms to minimize).
Can anybody help me?
Are there plans to improve the leastsq function?
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