generate_norm1_norm_constraint

(function from pyomo.contrib.mindtpy.util)

pyomo.contrib.mindtpy.util.generate_norm1_norm_constraint(model, setpoint_model, config, discrete_only=True)[source]

This function generates constraint (PF-OA main problem) for minimum Norm1 distance to setpoint_model.

Norm constraint is used to guarantees the monotonicity of the norm objective value sequence of all iterations.

Norm1 distance of \((x,y) = \sum_i |x_i - y_i|\). Ref: Paper ‘A storm of feasibility pumps for nonconvex MINLP’ Eq. (16).

Parameters:

  • model (Pyomo model) – The model that needs the norm constraint.

  • setpoint_model (Pyomo model) – The model that provides the base point for us to calculate the distance.

  • config (ConfigBlock) – The specific configurations for MindtPy.

  • discrete_only (bool, optional) – Whether to only optimize on distance between the discrete variables, by default True.