Fix NLOpt optimizers trying to iterate NoneType

qiskit/aqua/components/optimizers/nlopts/nloptimizer.py

@@ -93,6 +93,8 @@ def optimize(self, num_vars, objective_function, gradient_function=None,
                  variable_bounds=None, initial_point=None):
         super().optimize(num_vars, objective_function,
                          gradient_function, variable_bounds, initial_point)
+        if variable_bounds is None:
+            variable_bounds = [(None, None)] * num_vars
         return self._minimize(self._optimizer_names[self.get_nlopt_optimizer()],
                               objective_function,
                               variable_bounds,
@@ -101,7 +103,7 @@ def optimize(self, num_vars, objective_function, gradient_function=None,
     def _minimize(self,
                   name: str,
                   objective_function: Callable,
-                  variable_bounds: Optional[List[Tuple[float, float]]] = None,
+                  variable_bounds: Optional[List[Tuple[float, float]]],
                   initial_point: Optional[np.ndarray] = None,
                   max_evals: int = 1000) -> Tuple[float, float, int]:
         """Minimize using objective function

test/aqua/test_nlopt_optimizers.py

@@ -28,26 +28,29 @@
 class TestNLOptOptimizers(QiskitAquaTestCase):
     """ Test NLOpt Optimizers """
 
-    def _optimize(self, optimizer):
+    def _optimize(self, optimizer, use_bound):
         x_0 = [1.3, 0.7, 0.8, 1.9, 1.2]
-        bounds = [(-6, 6)] * len(x_0)
+        bounds = [(-6, 6)] * len(x_0) if use_bound else None
         res = optimizer.optimize(len(x_0), rosen, initial_point=x_0, variable_bounds=bounds)
         np.testing.assert_array_almost_equal(res[0], [1.0] * len(x_0), decimal=2)
         return res
 
     # ESCH and ISRES do not do well with rosen
     @idata([
-        [CRS],
-        [DIRECT_L],
-        [DIRECT_L_RAND],
+        [CRS, True],
+        [DIRECT_L, True],
+        [DIRECT_L_RAND, True],
+        [CRS, False],
+        [DIRECT_L, False],
+        [DIRECT_L_RAND, False],
     ])
     @unpack
-    def test_nlopt(self, optimizer_cls):
+    def test_nlopt(self, optimizer_cls, use_bound):
         """ NLopt test """
         try:
             optimizer = optimizer_cls()
             optimizer.set_options(**{'max_evals': 50000})
-            res = self._optimize(optimizer)
+            res = self._optimize(optimizer, use_bound)
             self.assertLessEqual(res[2], 50000)
         except MissingOptionalLibraryError as ex:
             self.skipTest(str(ex))