I Best way to fit three functions

[LCSphysicist]

TL;DR

I have basically three set of data, each set corresponding to a function, so that the data should fit the function, and each function is dependent of 4 parameters. How to find the best 4 parameters that fit it?

So I have $$f(x,y,z,t,n) = 0,g(x,y,z,t,n) = 0,h(x,y,z,t,n) = 0 $$ and i need to find the best $[x,y,z,t]$ that fit the data, where n is the variable. Now, the amount of data for each function is pretty low (2 pair for f (that is, two (n,f)), 3 pair for g and another 3 pair for h)

The main problem here is: the functions are highly non-linear. So i have no idea how can i write a program to find the best x,y,z,t!

I have thought of try to simultaneously minimize the residues, for example, for f:
$$R(f) = \sqrt{(f(x,y,z,t,n_1) - y_1)^2+(f(x,y,z,t,n_2) - y_2)^2}$$
(where y is the f obtained on thee data at n_1 (the pair i have cited above))

But this minimization subject to the condition that $R(g), R(h)$ (here, three terms) also be minimum. So maybe i could use Lagrangian method:
$$dR(h) = \lambda dR(g) + \mu dR(h)$$
But this is going to be extremelly massive and tedius to write a code, and the code will take days to run. As i said, the functions are non-linear.

Any method suggestion? or program suggestion?

 

[Maarten Havinga]

Extremely massive and tedious is probably the best you can get. If it was one occurrence, I'd advise WolframAlpha or plot the 3 functions and visually pick the best fit (further improved with Newton's approximation method).

For now I'd advise to combine the requirements for all the data points on f, g and h and rewrite them as $function - data = 0$ and then the square of the left hand side must be minimal for all data points simultaneously: so take the derivative with respect to $n$ of these squares all added up and approximate where this formula in $n$ equals 0 using Newton's approximation method or some better root finding algorithm.

Wiki on Newtons method

 

[mfb]

You'll need to decide what you consider "best fit". Write that down mathematically as fitquality(x,y,z,t), then feed it in a program for minimization.

The sum of squared residues for all data points (data point minus the applicable function) sounds like a good start.