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refactor: Simplify optimizer interface by removing ambiguous f_df parameter #486

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refactor: Simplify optimizer interface by removing ambiguous f_df parameter #486

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refactor: Simplify optimizer interface by removing ambiguous f_df par…
May 28, 2026
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apaleyes May 30, 2026
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12 changes: 6 additions & 6 deletions emukit/core/optimization/gradient_acquisition_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,14 +51,14 @@ def _optimize(self, acquisition: Acquisition, context_manager: ContextManager) -
x = np.array(context_manager.context_values)[None, :]
return x, f(x)

# Prepare gradient function (if available)
if acquisition.has_gradients:

def f_df(x):
def df(x):
f_value, df_value = acquisition.evaluate_with_gradients(x)
return -f_value, -df_value

# Return negated gradient (since we negated f)
return -df_value
else:
f_df = None
df = None

optimizer = self._get_optimizer(context_manager)
anchor_points_generator = ObjectiveAnchorPointsGenerator(self.space, acquisition, num_samples=self.num_samples)
Expand All @@ -70,7 +70,7 @@ def f_df(x):
optimized_points = []
for a in anchor_points:
optimized_point = apply_optimizer(
optimizer, a, space=self.space, f=f, df=None, f_df=f_df, context_manager=context_manager
optimizer, a, space=self.space, f=f, df=df, context_manager=context_manager
)
optimized_points.append(optimized_point)

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