The optimisation of the parameters of an arbitrary model is dependent on a function f which takes the current parameter values θ∈n and returns a single real value f (θ)∈ corresponding to position θ in the n-dimensional space. For it is that single value which is minimised as
|= argf (θ),||(14.1)|
where is the parameter vector which is equal to the argument which minimises the function f (θ). In most analyses in relax, f (θ) is the chi-squared equation
where i is the summation index over all data, yi is the experimental data, yi(θ) is the back calculated data, and σi is the experimental error.