11.3. Minimisation techniques

Warning

This method is entirely reliant on the Relauncher architecture so the only available constructor is the runner constructor (discussed in the section Construction with a TRun). This means there is no constructor based on TCode or function (respectively described in Construction with a TCode and Construction for a function with the Launcher architecture). This is because the method uses the Nlopt-algorithm library, introduced in The Reoptimizer module or the Vizir package for multi-criteria and many-objective algorithms.

Even though the theory behind this method is not revolutionary, these methods are of interest and are, historically and conceptually, among the simplest methods one can use. Because of the way the framework is organised, it can be used with all Relauncher assessors and can call any algorithm from NLopt or Vizir.

Apart from what is presented in Defining the TMinimisation properties, the TMinimisation class does not provide any additional options or methods beyond those available in the default TCalibration object (see Calibration classes, distance and likelihood functions, observations and model).

The usage of the TMinimisation class can be summarised in a few key steps:

  1. Prepare the data and the model:

    • Specify the experimental dataserver, the parameters to calibrate, and the model;

    • Construct the TMinimisation object with the appropriate distance function (see Constructing the TMinimisation object).

  2. Set the algorithm properties:

  3. Perform the estimate and analyse the results:

    • Run the estimate process;

    • Extract the results and visualise them with the standard plotting tools (see Looking at the results).

Two examples are also provided in the use-case section (see Macro “calibrationMinimisationFlowrate1D.C” and Macro “calibrationMinimisationFlowrate2DVizir.C”).