diff --git a/cython/gtsam/tests/test_DogLegOptimizer.py b/cython/gtsam/tests/test_DogLegOptimizer.py
new file mode 100644
index 000000000..20c8bfb94
--- /dev/null
+++ b/cython/gtsam/tests/test_DogLegOptimizer.py
@@ -0,0 +1,78 @@
+"""
+GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+See LICENSE for the license information
+
+DoglegOptimizer unit tests.
+Author: Frank Dellaert
+"""
+# pylint: disable=no-member, invalid-name
+import unittest
+
+import gtsam
+import numpy as np
+from gtsam.utils.test_case import GtsamTestCase
+
+
+class TestDoglegOptimizer(GtsamTestCase):
+
+    def test_DoglegOptimizer(self):
+        # Linearization point
+        T11 = gtsam.Pose2(0, 0, 0)
+        T12 = gtsam.Pose2(1, 0, 0)
+        T21 = gtsam.Pose2(0, 1, 0)
+        T22 = gtsam.Pose2(1, 1, 0)
+
+        # Factor graph
+        graph = gtsam.NonlinearFactorGraph()
+
+        # Priors
+        prior = gtsam.noiseModel_Isotropic.Sigma(3, 1)
+        graph.add(gtsam.PriorFactorPose2(11, T11, prior))
+        graph.add(gtsam.PriorFactorPose2(21, T21, prior))
+
+        # Odometry
+        model = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.01, 0.01, 1e6]))
+        graph.add(gtsam.BetweenFactorPose2(11, 12, T11.between(T12), model))
+        graph.add(gtsam.BetweenFactorPose2(21, 22, T21.between(T22), model))
+
+        # Range
+        model_rho = gtsam.noiseModel_Isotropic.Sigma(1, 0.01)
+        graph.add(gtsam.RangeFactorPose2(12, 22, 1.0, model_rho))
+
+        # Print graph
+        print(graph)
+
+        sigma = 0.1
+        values = gtsam.Values()
+        values.insert(11, T11.retract(np.random.normal(0, sigma, 3)))
+        values.insert(12, T12)
+        values.insert(21, T21)
+        values.insert(22, T22)
+        linearized = graph.linearize(values)
+
+        # Get Jacobian
+        ordering = gtsam.Ordering()
+        ordering.push_back(11)
+        ordering.push_back(21)
+        ordering.push_back(12)
+        ordering.push_back(22)
+        A, b = linearized.jacobian(ordering)
+        Q = np.dot(A.transpose(), A)
+        print(np.linalg.det(Q))
+
+        bn = linearized.eliminateSequential(ordering)
+
+        # Print gradient
+        linearized.gradientAtZero()
+
+        # Run dogleg optimizer
+        dl = gtsam.DoglegOptimizer(graph, values)
+        result = dl.optimize()
+        print(graph.error(result))
+
+
+if __name__ == "__main__":
+    unittest.main()