diff --git a/Wrappers/Python/cil/optimisation/operators/Operator.py b/Wrappers/Python/cil/optimisation/operators/Operator.py
index 8da95c6e73..8944441809 100644
--- a/Wrappers/Python/cil/optimisation/operators/Operator.py
+++ b/Wrappers/Python/cil/optimisation/operators/Operator.py
@@ -225,11 +225,9 @@ def PowerMethod(operator, max_iteration=10, initial=None, tolerance=1e-5,  retur
         diff = numpy.finfo('d').max
         i = 0
         while (i < max_iteration and diff > tolerance):
-            i+=1
             
             operator.direct(x0, out = y_tmp)
-
-            
+           
             if square:                
                 #swap datacontainer references
                 tmp = x0
diff --git a/Wrappers/Python/test/test_BlockOperator.py b/Wrappers/Python/test/test_BlockOperator.py
index bff62cd5af..adfac71572 100644
--- a/Wrappers/Python/test/test_BlockOperator.py
+++ b/Wrappers/Python/test/test_BlockOperator.py
@@ -187,9 +187,8 @@ def test_FiniteDiffOperator(self):
         G = FiniteDifferenceOperator(ig, direction=0, bnd_cond = 'Neumann')
         logging.info("{} {}".format(type(u), str(u.as_array())))    
         logging.info(str(G.direct(u).as_array()))
-        LinearOperator.PowerMethod(G, range_is_domain=False)
         # Gradient Operator norm, for one direction should be close to 2
-        numpy.testing.assert_allclose(LinearOperator.PowerMethod(G, range_is_domain=False), numpy.sqrt(4), atol=0.1)
+        numpy.testing.assert_allclose(G.norm(), numpy.sqrt(4), atol=0.1)
 
         M1, N1, K1 = 200, 300, 2
         ig1 = ImageGeometry(voxel_num_x = M1, voxel_num_y = N1, channels = K1)