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Fix overflow dual annealing #364

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Fix overflow dual annealing #364

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34aa6ce
prevent floating point overflow in dual annealing strategy
benvanwerkhoven Mar 10, 2026
76bc549
avoid division by zero in simulated annealing
benvanwerkhoven Mar 10, 2026
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3 changes: 2 additions & 1 deletion kernel_tuner/strategies/dual_annealing.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
"""The strategy that uses the dual annealing optimization method."""
import scipy.optimize
import numpy as np

from kernel_tuner.util import StopCriterionReached
from kernel_tuner.searchspace import Searchspace
Expand All @@ -16,7 +17,7 @@ def tune(searchspace: Searchspace, runner, tuning_options):
method, max_fevals = common.get_options(tuning_options.strategy_options, _options)

#scale variables in x to make 'eps' relevant for multiple variables
cost_func = CostFunc(searchspace, tuning_options, runner, scaling=True)
cost_func = CostFunc(searchspace, tuning_options, runner, scaling=True, invalid_value=np.inf)

bounds, x0, _ = cost_func.get_bounds_x0_eps()

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4 changes: 2 additions & 2 deletions kernel_tuner/strategies/simulated_annealing.py
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Expand Up @@ -96,7 +96,7 @@ def acceptance_prob(old_cost, new_cost, T):
# if start pos is not valid, always move
if isinstance(old_cost, ErrorConfig):
res = 1.0
# if we have found a valid ps before, never move to nonvalid pos
# if we have found a valid pos before, never move to nonvalid pos
elif isinstance(new_cost, ErrorConfig):
res = 0.0
# always move if new cost is better
Expand All @@ -108,7 +108,7 @@ def acceptance_prob(old_cost, new_cost, T):
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)
rel_diff = abs_diff / (np.abs(old_cost) if old_cost != 0.0 else 1e-20)

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