This function logs potentially poorly scaled parts of the model.
It requires that all variables be bounded.
It is important to note that this check is neither necessary nor sufficient
to ensure a well-scaled model. However, it is a useful tool to help identify
problematic parts of a model.
This function uses symbolic differentiation and interval arithmetic
to compute bounds on each entry in the jacobian of the constraints.
Note that logging has to be turned on to get the output
- Parameters:
m (BlockData) – The pyomo model or block
too_large (float) – Values above too_large will generate a log entry
too_small (float) – Coefficients below too_small will generate a log entry
- Returns:
success – Returns False if any potentially poorly scaled components were found
- Return type:
bool