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Fix bug in simulated annealing when dealing with negative objectives #331

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Fix bug in simulated annealing when dealing with negative objectives #331

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ac6900f
Fix bug in simulated annealing when dealing with negative objectives
stijnh Sep 30, 2025
810ffe3
Remove unused `tuning_options` parameter to keep SonarQube happy
stijnh Sep 30, 2025
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16 changes: 10 additions & 6 deletions kernel_tuner/strategies/simulated_annealing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@ def tune(searchspace: Searchspace, runner, tuning_options):
print(e)
return cost_func.results

ap = acceptance_prob(old_cost, new_cost, T, tuning_options)
ap = acceptance_prob(old_cost, new_cost, T)
r = random.random()

if ap > r:
Expand Down Expand Up @@ -90,7 +90,7 @@ def tune(searchspace: Searchspace, runner, tuning_options):

tune.__doc__ = common.get_strategy_docstring("Simulated Annealing", _options)

def acceptance_prob(old_cost, new_cost, T, tuning_options):
def acceptance_prob(old_cost, new_cost, T):
"""Annealing equation, with modifications to work towards a lower value."""
res = 0.0
# if start pos is not valid, always move
Expand All @@ -104,10 +104,14 @@ def acceptance_prob(old_cost, new_cost, T, tuning_options):
res = 1.0
# maybe move if old cost is better than new cost depending on T and random value
else:
if tuning_options.objective_higher_is_better:
res = np.exp(((new_cost-old_cost)/new_cost)/T)
else:
res = np.exp(((old_cost-new_cost)/old_cost)/T)
# we must have abs_diff <= 0, as new_cost >= old_cost
abs_diff = old_cost - new_cost

# relative to abs(old_cost), as the cost might be negative
rel_diff = abs_diff / np.abs(old_cost)

# exponential decay
res = np.exp(rel_diff / T)
return res


Expand Down