Update acquisition.py

Computational-efficiency of the MIP identification increased

Author: udicaprio

bayes_opt/acquisition.py

@@ -308,28 +308,38 @@ def _l_bfgs_b_minimize(
         min_acq: float | None = None
         x_try: NDArray[Float]
         x_min: NDArray[Float]
-        for x_try in x_seeds:
 
-            # def continuous_acq(x: NDArray[Float], x_try=x_try) -> NDArray[Float]:
-            #     x_try[continuous_dimensions] = x
-            #     return acq(x_try)
-            if all(continuous_dimensions):
-            # Find the minimum of minus the acquisition function
+        #Case of continous optimization
+        if all(continuous_dimensions):
+            for x_try in x_seeds:
                 res: OptimizeResult = minimize(acq, x_try, bounds=continuous_bounds, method="L-BFGS-B")
-            else:
-                res: OptimizeResult = differential_evolution(acq, bounds=bounds,
+                if not res.success:
+                    continue
+
+                # Store it if better than previous minimum(maximum).
+                if min_acq is None or np.squeeze(res.fun) >= min_acq:
+                    x_try = res.x
+                    x_min = x_try
+                    min_acq = np.squeeze(res.fun)
+        
+        #Case of mixed-integer optimization
+        else:
+            ntrials = max(1, len(x_seeds)//100)
+            for i in range(ntrials):
+                xinit = space.random_sample(15*len(space.bounds), random_state=i)
+                res: OptimizeResult = differential_evolution(acq, bounds=bounds, init=xinit,
                                                              integrality=discrete_dimensions,
                                                              seed=self.random_state)
-            # See if success
-            if not res.success:
-                continue
-
-            # Store it if better than previous minimum(maximum).
-            if min_acq is None or np.squeeze(res.fun) >= min_acq:
-                x_try = res.x
-                x_min = x_try
-                min_acq = np.squeeze(res.fun)
-
+                # See if success
+                if not res.success:
+                    continue
+
+                # Store it if better than previous minimum(maximum).
+                if min_acq is None or np.squeeze(res.fun) >= min_acq:
+                    x_try = res.x
+                    x_min = x_try
+                    min_acq = np.squeeze(res.fun)
+        
         if min_acq is None:
             min_acq = np.inf
             x_min = np.array([np.nan] * space.bounds.shape[0])