Question: How do I do a non-linear least square fit in maple, like the Levenberg-Marquardt, when I need to run a code to compute the objective function ?

I have 6 parameters, say p1 - p6, which enter my Monte Carlo Code and computes a 1-D Array of floats of size 1000 . I have an experimental array of floats of the same size. My code outputs an array which is the difference of these two arrays. The objective function is the sum of the sqares of the elements in this output array. I have initial guess for the parameters: p1i - p6i. I need to find the values of the 6 parameters which minimize the objective function.

Obviously I do not have an explicit mathematical form for the objective function and nor its Jacobian.

Could some one please help me in this task.  Thanks in advance. 

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