[SciPy-User] Alternatives to scipy.optimize

[Erik Petigura]

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. 

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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