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Optimization Problem - Need Stochastic Method

Hi All,

I just started using Mathematica - and I'm hoping that there's a nice trick
I can use to get my optimization done.

The problem is in stereology - I'm an engineering grad student and I have
developed a bivariate size-orientation unfolding equation for cylinders.
The equation is extremely complex - a double-integral equation of about 14
terms.  In addition, there is a stability problem, meaning that for
practical purposes, the problem is ill-posed.

Because I need to graduate (this is basically a side project gone mad) I am
getting around the problems of finding a solution and dealing with the
ill-posedness by simply fitting a distribution that takes a form similar to
a bivariate normal distribution to the equation.  This means that I have to
fit 5 distribution parameters to the experimental data, of which there are
about 25 points.  This makes for a well-overdetermined system, and I have
constructed a square-error function in Mathematica that is a function of the
parameters I need to optimize.

Here's the problem: I tried to use constrained NMinimize, but it did not
converge well - when I plotted the solution graphically against the data
points, there was severe and systematic error.  I have no idea how the
fitting function behaves since it's impossible to visualize, but as
described earlier it's extremely complicated when written out, and it
involves trig functions and exponentials, so I wouldn't assume that there
are no local minima.  I think that the best thing to do is to use a
stochastic method as the search algorithm instead of the default
Nelder-Mead, but I am not sure if one exists.

Can anyone help?


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