[SciPy-User] Alternatives to scipy.optimize

[josef.pktd at gmail.com]

On Sat, Feb 25, 2012 at 2:17 PM, Erik Petigura <eptune at 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.

Josef

>
> 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
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>