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→Target functions: Formatting.
* This takes the input data and implements functions, gradients and Hessians for the chi-squared optimisation function for the analysis.
* The first and second partial derivatives of the equations should be calculated by hand (computer algebra system (CAS) software such as maxima, mathematica, etc. can do this easily). The availability of gradients and Hessians opens up the possibility to use Newton optimisation which, speed- and precision-wise, is well worth the effort! It also allows for the use of the Method of Multipliers (or Augmented Lagrangian) constraint algorithm which, if parameter constraints are required, e.g. 0 <= ≤ {{:S2 <= }} ≤ 1, is an incredible algorithm.
== Result visualisation ==
