diff --git a/proxtorch/operators/graphnet.py b/proxtorch/operators/graphnet.py
index dd52457..082500b 100644
--- a/proxtorch/operators/graphnet.py
+++ b/proxtorch/operators/graphnet.py
@@ -15,9 +15,9 @@ def prox(self, x: torch.Tensor, tau: float) -> torch.Tensor:
 
     def _smooth(self, x: torch.Tensor) -> torch.Tensor:
         # The last channel is the for the l1 norm
-        grad = self.gradient(x)[:-1]
-        # norm of the gradient
-        norm = torch.norm(grad) ** 2
+        grad = self.gradient(x)[:-1]/(1-self.l1_ratio)
+        # sum of squares of the gradients
+        norm = torch.sum(grad ** 2)
         return 0.5 * norm * self.alpha * (1 - self.l1_ratio)
 
     def _nonsmooth(self, x: torch.Tensor) -> torch.Tensor:
diff --git a/proxtorch/operators/tv_2d.py b/proxtorch/operators/tv_2d.py
index 77dfef4..afeffab 100644
--- a/proxtorch/operators/tv_2d.py
+++ b/proxtorch/operators/tv_2d.py
@@ -11,4 +11,4 @@ def __init__(self, alpha: float, max_iter: int = 200, tol: float = 1e-4) -> None
             max_iter (int, optional): Maximum iterations for the iterative algorithm. Defaults to 50.
             tol (float, optional): Tolerance level for early stopping. Defaults to 1e-2.
         """
-        super().__init__(alpha, max_iter, tol, l1_ratio=0.0)
+        super().__init__(alpha, l1_ratio=0.0, max_iter=max_iter, tol=tol)
diff --git a/proxtorch/operators/tv_3d.py b/proxtorch/operators/tv_3d.py
index 001e781..2f5ab8b 100644
--- a/proxtorch/operators/tv_3d.py
+++ b/proxtorch/operators/tv_3d.py
@@ -11,4 +11,4 @@ def __init__(self, alpha: float, max_iter: int = 200, tol: float = 1e-4) -> None
             max_iter (int, optional): Maximum iterations for the iterative algorithm. Defaults to 50.
             tol (float, optional): Tolerance level for early stopping. Defaults to 1e-2.
         """
-        super().__init__(alpha, max_iter, tol, l1_ratio=0.0)
+        super().__init__(alpha, l1_ratio=0.0, max_iter=max_iter, tol=tol)
diff --git a/proxtorch/operators/tvl1_3d.py b/proxtorch/operators/tvl1_3d.py
index 9d3efe3..45ace7c 100644
--- a/proxtorch/operators/tvl1_3d.py
+++ b/proxtorch/operators/tvl1_3d.py
@@ -41,9 +41,9 @@ class TVL1_3D(ProxOperator):
     def __init__(
         self,
         alpha: float,
+        l1_ratio=0.0,
         max_iter: int = 200,
         tol: float = 1e-4,
-        l1_ratio=0.0,
     ) -> None:
         """
         Initialize the 3D Total Variation proximal operator.
diff --git a/test/test_graphnet.py b/test/test_graphnet.py
index 740997c..eba82b9 100644
--- a/test/test_graphnet.py
+++ b/test/test_graphnet.py
@@ -3,57 +3,58 @@
 from proxtorch.operators import GraphNet3D, GraphNet2D
 
 
-#
-# def test_converges_to_sparse_smooth():
-#     torch.manual_seed(0)
-#
-#     # generate a spatially sparse signal
-#     x_true = torch.zeros(10, 10)
-#     x_true[3:7, 3:7] = torch.ones(4, 4)
-#     x_true = x_true.flatten()
-#
-#     # generate a random matrix
-#     A = torch.rand(100, 100)
-#
-#     # generate measurements
-#     y = A @ x_true
-#
-#     # define the proximal operator
-#     alpha = 10
-#     l1_ratio = 0.0 # 0.5
-#     prox = GraphNet2D(alpha, l1_ratio)
-#
-#     # define the objective function
-#     def objective(x):
-#         return 0.5 * torch.norm(A @ x.reshape(-1) - y) ** 2
-#
-#     # define the step size
-#     tau = 1 / torch.norm(A.t() @ A)
-#
-#     # initialize the solution
-#     x = torch.nn.Parameter(torch.rand(10, 10, requires_grad=True))
-#
-#     # optimizer
-#     optimizer = torch.optim.SGD([x], lr=tau)
-#
-#     # optimization loop
-#     for i in range(1000):
-#         optimizer.zero_grad()
-#         obj = objective(x) + prox(x)
-#         obj.backward()
-#         optimizer.step()
-#         x.data = prox.prox(x.data, tau)
-#
-#     # check that the result is smooth
-#     plt.imshow(x.data.detach().numpy())
-#     plt.show()
-#
-#     # compare with x_true
-#     difference = torch.norm(x.data.flatten() - x_true)
-#     assert difference < 1e-3
-#
-#     # check that the result is sparse
-#     assert torch.sum(x.data == 0) > 50
+def test_converges_to_sparse_smooth():
+    import matplotlib.pyplot as plt
+    torch.manual_seed(0)
+
+    # generate a spatially sparse signal
+    x_true = torch.zeros(10, 10)
+    x_true[3:7, 3:7] = torch.ones(4, 4)
+    x_true = x_true.flatten()
+
+    # generate a random matrix
+    A = torch.rand(100, 100)
+
+    # generate measurements
+    y = A @ x_true
+
+    # define the proximal operator
+    alpha = 1000
+    l1_ratio = 0.0  # 0.5
+    prox = GraphNet2D(alpha, l1_ratio)
+
+    # define the objective function
+    def objective(x):
+        return 0.5 * torch.norm(A @ x.reshape(-1) - y) ** 2
+
+    # define the step size
+    tau = 0.1 / torch.norm(A.t() @ A)
+
+    # initialize the solution
+    x = torch.nn.Parameter(torch.rand(10, 10, requires_grad=True))
+
+    # optimizer
+    optimizer = torch.optim.SGD([x], lr=tau, nesterov=True, momentum=0.9)
+
+    # optimization loop
+    for i in range(20000):
+        optimizer.zero_grad()
+        p=prox(x)
+        obj = objective(x) + prox(x)
+        obj.backward()
+        optimizer.step()
+        x.data = prox.prox(x.data, tau)
+
+    # check that the result is smooth
+    plt.imshow(x.data.detach().numpy())
+    plt.show()
+
+    # compare with x_true
+    difference = torch.norm(x.data.flatten() - x_true)
+    assert difference < 1e-3
+
+    # check that the result is sparse
+    assert torch.sum(x.data == 0) > 50
 
 
 def test_graph_net_3d_prox():