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diff --git a/tensorflow/compiler/tests/proximal_gradient_descent_test.py b/tensorflow/compiler/tests/proximal_gradient_descent_test.py
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+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Proximal Gradient Descent optimizer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.compiler.tests import xla_test
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+from tensorflow.python.training import gradient_descent
+from tensorflow.python.training import proximal_gradient_descent
+
+
+class ProximalGradientDescentOptimizerTest(xla_test.XLATestCase):
+
+  def testResourceProximalGradientDescentwithoutRegularization(self):
+    with self.test_session(), self.test_scope():
+      var0 = resource_variable_ops.ResourceVariable([0.0, 0.0])
+      var1 = resource_variable_ops.ResourceVariable([0.0, 0.0])
+      grads0 = constant_op.constant([0.1, 0.2])
+      grads1 = constant_op.constant([0.01, 0.02])
+      opt = proximal_gradient_descent.ProximalGradientDescentOptimizer(
+          3.0, l1_regularization_strength=0.0, l2_regularization_strength=0.0)
+      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+      variables.global_variables_initializer().run()
+
+      self.assertAllClose([0.0, 0.0], var0.eval())
+      self.assertAllClose([0.0, 0.0], var1.eval())
+
+      # Run 3 steps Proximal Gradient Descent.
+      for _ in range(3):
+        update.run()
+
+      self.assertAllClose(np.array([-0.9, -1.8]), var0.eval())
+      self.assertAllClose(np.array([-0.09, -0.18]), var1.eval())
+
+  def testProximalGradientDescentwithoutRegularization2(self):
+    with self.test_session(), self.test_scope():
+      var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
+      var1 = resource_variable_ops.ResourceVariable([4.0, 3.0])
+      grads0 = constant_op.constant([0.1, 0.2])
+      grads1 = constant_op.constant([0.01, 0.02])
+
+      opt = proximal_gradient_descent.ProximalGradientDescentOptimizer(
+          3.0, l1_regularization_strength=0.0, l2_regularization_strength=0.0)
+      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+      variables.global_variables_initializer().run()
+
+      self.assertAllClose([1.0, 2.0], var0.eval())
+      self.assertAllClose([4.0, 3.0], var1.eval())
+
+      # Run 3 steps Proximal Gradient Descent
+      for _ in range(3):
+        update.run()
+
+      self.assertAllClose(np.array([0.1, 0.2]), var0.eval())
+      self.assertAllClose(np.array([3.91, 2.82]), var1.eval())
+
+  def testProximalGradientDescentWithL1(self):
+    with self.test_session(), self.test_scope():
+      var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
+      var1 = resource_variable_ops.ResourceVariable([4.0, 3.0])
+      grads0 = constant_op.constant([0.1, 0.2])
+      grads1 = constant_op.constant([0.01, 0.02])
+
+      opt = proximal_gradient_descent.ProximalGradientDescentOptimizer(
+          3.0, l1_regularization_strength=0.001, l2_regularization_strength=0.0)
+      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+      variables.global_variables_initializer().run()
+
+      self.assertAllClose([1.0, 2.0], var0.eval())
+      self.assertAllClose([4.0, 3.0], var1.eval())
+
+      # Run 10 steps proximal gradient descent.
+      for _ in range(10):
+        update.run()
+
+      self.assertAllClose(np.array([-1.988, -3.988001]), var0.eval())
+      self.assertAllClose(np.array([3.67, 2.37]), var1.eval())
+
+  def testProximalGradientDescentWithL1_L2(self):
+    with self.test_session(), self.test_scope():
+      var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
+      var1 = resource_variable_ops.ResourceVariable([4.0, 3.0])
+      grads0 = constant_op.constant([0.1, 0.2])
+      grads1 = constant_op.constant([0.01, 0.02])
+
+      opt = proximal_gradient_descent.ProximalGradientDescentOptimizer(
+          3.0, l1_regularization_strength=0.001, l2_regularization_strength=2.0)
+      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+      variables.global_variables_initializer().run()
+
+      self.assertAllClose([1.0, 2.0], var0.eval())
+      self.assertAllClose([4.0, 3.0], var1.eval())
+
+      # Run 10 steps Proximal Gradient Descent
+      for _ in range(10):
+        update.run()
+
+      self.assertAllClose(np.array([-0.0495, -0.0995]), var0.eval())
+      self.assertAllClose(np.array([-0.0045, -0.0095]), var1.eval())
+
+  def applyOptimizer(self, opt, steps=5):
+    var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
+    var1 = resource_variable_ops.ResourceVariable([3.0, 4.0])
+    grads0 = constant_op.constant([0.1, 0.2])
+    grads1 = constant_op.constant([0.01, 0.02])
+
+    update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
+    variables.global_variables_initializer().run()
+
+    self.assertAllClose([1.0, 2.0], var0.eval())
+    self.assertAllClose([3.0, 4.0], var1.eval())
+
+    # Run ProximalAdagrad for a few steps
+    for _ in range(steps):
+      update.run()
+
+    return var0.eval(), var1.eval()
+
+  def testEquivGradientDescentwithoutRegularization(self):
+    with self.test_session(), self.test_scope():
+      val0, val1 = self.applyOptimizer(
+          proximal_gradient_descent.ProximalGradientDescentOptimizer(
+              3.0,
+              l1_regularization_strength=0.0,
+              l2_regularization_strength=0.0))
+
+    with self.test_session(), self.test_scope():
+      val2, val3 = self.applyOptimizer(
+          gradient_descent.GradientDescentOptimizer(3.0))
+
+    self.assertAllClose(val0, val2)
+    self.assertAllClose(val1, val3)
+
+
+if __name__ == "__main__":
+  test.main()