Fix bug in simulated annealing when dealing with negative objectives

Author: stijnh

kernel_tuner/strategies/simulated_annealing.py

@@ -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