L2_regularized_objective

(function from pyomo.contrib.parmest.parmest)

pyomo.contrib.parmest.parmest.L2_regularized_objective(model, prior_FIM, theta_ref=None, regularization_weight=1.0, obj_function=<function SSE>)[source]

Calculates objective + regularization_weight*(theta - theta_ref)^T * prior_FIM * (theta - theta_ref) using label-based alignment for safety and subsets for efficiency.

Parameters:

  • model (ConcreteModel) – Annotated Pyomo model

  • prior_FIM (pd.DataFrame) – Prior Fisher Information Matrix from previous experimental design

  • theta_ref (dict, optional) – Reference parameter values used in regularization. If None, defaults to the current parameter values in the model.

  • regularization_weight (float, optional) – Weighting factor for the regularization term. Default is 1.0.

  • obj_function (callable, optional) – Built-in objective function selected by the user, e.g., SSE. Default is SSE.

Returns:

expr – Expression representing the L2-regularized objective

Return type:

Pyomo expression