[SciPy-User] Alternatives to scipy.optimize

josef.pktd@gmai... josef.pktd@gmai...
Sat Feb 25 13:34:00 CST 2012

On Sat, Feb 25, 2012 at 2:17 PM, Erik Petigura <eptune@gmail.com> wrote:
> Dear Scipy,
> Up until now, I've found the optimize module very useful.  Now, I'm finding
> that I need finer control.  I am fitting a model to data that is of the
> following from:
> model = func1(p1) + func2(p2)
> func1 is nonlinear in its parameters and func2 is linear in its parameters.
> There are two things I am struggling with:
> 1. I'd like to find the best fit parameters for func1 using an iterative
> approach (e.g. simplex algorithm that changes p1.).  At each iteration, I
> want to compute the optimum p2 by linear least squares in the interest of
> speed and robustness.

you can still do this with any regular optimizer like optimize.fmin,
just calculate the linear solution inside the outer function that is
optimized by fmin.

I haven't seen any python package yet, that would estimate partial
linear models.

If you find a solution, then I would be interested in it for statsmodels.


> 2. I'd also like the ability to hold certain parameters fixed in the
> optimization with out redefining my objective function each time.
> Is there another module you would recommend?  I've found openopt, but I
> wanted to get some guidance before I dive in to that.
> Erik
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