- #1

- 5

- 0

Code:

```
model = a/Sqrt[4 k w^2 + (v^2 - w^2)^2]
data = {{242, 7/10}, {244.3`, 1}, {245.33`, 3/2}, {245.54`,
2}, {248.63`, 4}, {249.6`, 5}, {250.48`, 34/5}, {250.91`,
8}, {251.18`, 9}, {251.35`, 10}, {252.76`, 20}, {253.25`,
40}, {253.5`, 55}, {253.6`, 70}, {253.7`, 75}, {253.9`,
77}, {254.16`, 66}, {254.5`, 47}, {254.86`, 33}, {255.82`,
15}, {257, 5}};
```

I need to find the parameters that give the best fit for the data. This is the command I think is the closest to the solution:

Code:

```
fit = FindFit[
data, {model, {0 <= k}}, {{a, 30}, {k, 0.007}, {v, 253}}, w}
```

but i get the error:

Code:

`FindFit::eit: The algorithm does not converge to the tolerance of 4.806217383937354`*^-6 in 500 iterations. The best estimated solution, with feasibility residual, KKT residual or complementary residual of {...} is returned`

I have tried playing with starting values, conditionals, NonlinearModelFit and others but none of them work. Could someone please help?