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Pyomo.DoE: Allow user to define experiment cost to include in the objective #3527

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Pyomo.DoE: Allow user to define experiment cost to include in the objective #3527

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86c0906
Experiment with adding cost to the objective
adowling2 Mar 19, 2025
bcdfa57
Minor change on my office desktop
adowling2 Mar 26, 2025
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27 changes: 24 additions & 3 deletions pyomo/contrib/doe/doe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,6 +80,7 @@ def __init__(
tee=False,
get_labeled_model_args=None,
logger_level=logging.WARNING,
experiment_cost_weight=None,
_Cholesky_option=True,
_only_compute_fim_lower=True,
):
Expand Down Expand Up @@ -134,6 +135,10 @@ def __init__(
Solver option to be passed for verbose output.
get_labeled_model_args:
Additional arguments for the ``get_labeled_model`` function on the Experiment object.
experiment_cost_weight:
This is the weight to add "experiment_cost" to the objective function.
This is important if the FIM objective is "flat" in the decision space.
Default is None, which means no additional penalty term is added.
_Cholesky_option:
Boolean value of whether or not to use the cholesky factorization to compute the
determinant for the D-optimality criteria. This parameter should not be changed
Expand Down Expand Up @@ -210,6 +215,9 @@ def __init__(
# Empty results object
self.results = {}

# extended objective
self.experiment_cost_weight = experiment_cost_weight

# May need this attribute for more complicated structures?
# (i.e., no model rebuilding for large models with sequential)
self._built_scenarios = False
Expand Down Expand Up @@ -1238,12 +1246,25 @@ def determinant_general(b):
)
return m.determinant == det_perm

if self.experiment_cost_weight:
# If the experiment cost weight is provided, add it to the objective
# TODO: Add error checking that the experiment object computes an experiment cost
model.extra_objective = pyo.Expression(
expr=self.experiment_cost_weight*model.scenario_blocks[0].experiment_cost
)
else:
# If no experiment cost weight is provided, set it to 0
model.extra_objective = 0

if self.Cholesky_option and self.objective_option == ObjectiveLib.determinant:
model.obj_cons.cholesky_cons = pyo.Constraint(
model.parameter_names, model.parameter_names, rule=cholesky_imp
)



model.objective = pyo.Objective(
expr=2 * sum(pyo.log10(model.L[j, j]) for j in model.parameter_names),
expr=2 * sum(pyo.log10(model.L[j, j]) for j in model.parameter_names) + model.extra_objective,
sense=pyo.maximize,
)

Expand All @@ -1254,15 +1275,15 @@ def determinant_general(b):
)
model.obj_cons.determinant_rule = pyo.Constraint(rule=determinant_general)
model.objective = pyo.Objective(
expr=pyo.log10(model.determinant + 1e-6), sense=pyo.maximize
expr=pyo.log10(model.determinant + 1e-6) + model.extra_objective, sense=pyo.maximize
)

elif self.objective_option == ObjectiveLib.trace:
# if not determinant or cholesky, calculating the OBJ with trace
model.trace = pyo.Var(initialize=np.trace(fim), bounds=(small_number, None))
model.obj_cons.trace_rule = pyo.Constraint(rule=trace_calc)
model.objective = pyo.Objective(
expr=pyo.log10(model.trace), sense=pyo.maximize
expr=pyo.log10(model.trace) + model.extra_objective, sense=pyo.maximize
)

elif self.objective_option == ObjectiveLib.zero:
Expand Down