diff --git a/test/test_optimizers.py b/test/test_optimizers.py
index 263eb3bf..b19044be 100644
--- a/test/test_optimizers.py
+++ b/test/test_optimizers.py
@@ -221,10 +221,10 @@ def test_sr3_bad_parameters(optimizer, params):
 
 
 @pytest.mark.parametrize(
-    ["thresholder", "expected"], [("l0", 4), ("l1", 6), ("l2", 10)]
+    ["thresholder", "expected"], [("l0", 4), ("l1", 16), ("l2", 68)]
 )
 def test_get_regularization_1d(thresholder, expected):
-    data = np.array([[0, 1, 2]]).T
+    data = np.array([[0, 3, 5]]).T
     lam = 2
 
     reg = SR3.get_regularization(thresholder)
@@ -233,10 +233,10 @@ def test_get_regularization_1d(thresholder, expected):
 
 
 @pytest.mark.parametrize(
-    ["thresholder", "expected"], [("l0", 8), ("l1", 10), ("l2", 14)]
+    ["thresholder", "expected"], [("l0", 8), ("l1", 52), ("l2", 408)]
 )
 def test_get_regularization_2d(thresholder, expected):
-    data = np.array([[0, 1, 2], [1, 1, 0]]).T
+    data = np.array([[0, 3, 5], [7, 11, 0]]).T
     lam = 2
 
     reg = SR3.get_regularization(thresholder)
@@ -246,11 +246,11 @@ def test_get_regularization_2d(thresholder, expected):
 
 @pytest.mark.parametrize(
     ["thresholder", "expected"],
-    [("weighted_l0", 1.5), ("weighted_l1", 2), ("weighted_l2", 3)],
+    [("weighted_l0", 2.5), ("weighted_l1", 8.5), ("weighted_l2", 30.5)],
 )
 def test_get_weighted_regularization_1d(thresholder, expected):
-    data = np.array([[0, 1, 2]]).T
-    lam = np.array([[1, 1, 0.5]]).T
+    data = np.array([[0, 3, 5]]).T
+    lam = np.array([[3, 2, 0.5]]).T
 
     reg = SR3.get_regularization(thresholder)
     result = reg(data, lam)
@@ -259,11 +259,11 @@ def test_get_weighted_regularization_1d(thresholder, expected):
 
 @pytest.mark.parametrize(
     ["thresholder", "expected"],
-    [("weighted_l0", 2.5), ("weighted_l1", 3.5), ("weighted_l2", 5.5)],
+    [("weighted_l0", 16.5), ("weighted_l1", 158.5), ("weighted_l2", 1652.5)],
 )
 def test_get_weighted_regularization_2d(thresholder, expected):
-    data = np.array([[0, 1, 2], [2, 1, 0]]).T
-    lam = np.array([[1, 1, 0.5], [0.5, 0.5, 1]]).T
+    data = np.array([[0, 3, 5], [7, 11, 0]]).T
+    lam = np.array([[3, 2, 0.5], [1, 13, 17]]).T
 
     reg = SR3.get_regularization(thresholder)
     result = reg(data, lam)