Can LVs minimization functions minimize a function with a summation in it?

Hello all,
I am trying to minimize U-X, where U is a n point array of measured data, and X=f(U), a n-point summation function of 7 variables:
I have made efficient sub-VI's to calculate this function, and normally would solve this using an amoeba like approach, however, LV's minimization routines appear to need analytic functions. I can write this equation as above, but am not sure that I can provide this as an input to a minimization routine.
I am wondering:
1) Can this function be entered analytically to LV, allowing me to use built in solvers? If so, how?
2) If not, how can I minimize this? I have seen some very complicated and only modestly explained Lev-Mar examples, but can't make heads or tails of them.
A note: I am actually only trying to fit the baseline of the data, not the whole spectrum, but if someone can point the way on the minimization approach, I can figure out the rest (I may use a minimum entropy approach, i.e. minimizing the derivative of the sum of the difference between the data and the results of the function call).
I include my function VI below, as well as an ascii string of typical data, 5000 points long. Any ideas would be strongly appreciated!
Thanks,
RipRock
PS: I know I am asking a lot here. Serious thanks in advance.
Solved!
Go to Solution.
Attachments:
DashPotCorr1a.vi ‏72 KB
ExampleData.txt ‏47 KB

Hi all,
One more thing - to help out a bit more, here is a simple VI to read in the data, calculate the correction, and plot the two. I have provided default values that more or less work for this data (manual minimization!).
Thanks,
RipRock
PS: Here is what the result looks like:
Attachments:
ExampleCode.vi ‏22 KB

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