- #1

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- TL;DR Summary
- 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?

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?