Partition the in-scope variables of the input model
according to known nonadjustability to the uncertain parameters.
The result is referred to as the “effective” variable
partitioning.
In addition to the first-stage variables,
some of the variables considered second-stage variables
or state variables according to the user-provided variable
partitioning may be nonadjustable. This method analyzes
the decision rule order, fixed variables, and,
through an iterative pretriangularization method,
the equality constraints, to identify nonadjustable variables.
- Parameters:
model_data (model data object) – Main model data object.
- Returns:
effective_partitioning – Effective variable partitioning.
- Return type:
VariablePartitioning