[SciPy-User] Alternatives to scipy.optimize
Sat Feb 25 14:07:45 CST 2012
On Sat, Feb 25, 2012 at 11:17 AM, Erik Petigura <firstname.lastname@example.org> 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.
Are p1 and p2 coupled somehow? They must be, or computing p2 at each
iteration wouldn't be relevant.
This seems like a modification that could be made to the source code
without too much trouble, if iirc. Alternatively, you could possibly
use the callback option in fmin.
> 2. I'd also like the ability to hold certain parameters fixed in the
> optimization with out redefining my objective function each time.
Could you pass in a lambda function with those parameters fixed?
> Is there another module you would recommend? I've found openopt, but I
> wanted to get some guidance before I dive in to that.
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