create a function from data (that probably doesn't fit) using many many many calibrating parameters

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I have the following:

$\lambda_1 = \frac{const_{A}}{value1_1} + \frac{const_{B}}{value2_1} + \frac{const_{c}}{value3_1} $

$\lambda_2 = \frac{const_{A}}{value1_2} + \frac{const_{B}}{value2_2} + \frac{const_{c}}{value3_2} $

$ ... $

$\lambda_n = \frac{const_{A}}{value1_n} + \frac{const_{B}}{value2_n} + \frac{const_{c}}{value3_n} $

Where $n \approx 400$, and $(value_{1_n}, value_{2_n}, value_{3_n})$ alongside all $\lambda_n$ are known values.


Aim

I aim to create a function from this set of data that correctly describes $\lambda(n)$ for all n.

ie I think the solution's final form may take...

$\lambda_n = \left( \lambda_n = \frac{const_{A}}{value1_n} + \frac{const_{B}}{value2_n} + \frac{const_{c}}{value3_n} \right) * [SomeComplicatedCalibrator]$


Please could someone help? A full solution would be literally amazing, but ideas welcome :)


If someone could reply with suitable tags, I'll update the post too. I literally don't know what to even put haha.

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I don’t think it’s possible through the way you’re trying. If you know the results, and you know the denominators; then you can multiply every result by every denominator, which will sort of simplify searching for what the constants A B and C may be. If there were only two constants, X and Y, then that would be a function Z(n) = blahX + blahblahY. With 400 equations to satisify, it seems too easy to have at least two contradicting Z(n), such that no single values to X and Y can satisfy all 400 of the equations. And that’s for just two variables; you ask for three.

Instead, I suppose, I recommend you learn about generating functions. They take some selected set of numbers, such as your lambda(n), and assign them as the coefficients to a Taylor series, and then attempt to confusingly reverse-engineer it to find whatever function has that Taylor series, since every Taylor series is unique.

This isn’t exactly an answer, but I don’t have enough renown to comment. My apologies.