diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index fbe171b4b1ab1..2e8a9e99f6d01 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -109,6 +109,37 @@ public void distributedLDAModel() {
     assert(model.logPrior() < 0.0);
   }
 
+
+  @Test
+  public void OnlineOptimizerCompatibility() {
+      int k = 3;
+      double topicSmoothing = 1.2;
+      double termSmoothing = 1.2;
+
+      // Train a model
+      OnlineLDAOptimizer op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51)
+              .setGammaShape(1e40).setMiniBatchFraction(0.5);
+      LDA lda = new LDA();
+      lda.setK(k)
+         .setDocConcentration(topicSmoothing)
+         .setTopicConcentration(termSmoothing)
+         .setMaxIterations(5)
+         .setSeed(12345)
+         .setOptimizer(op);
+
+      LDAModel model = lda.run(corpus);
+
+      // Check: basic parameters
+      assertEquals(model.k(), k);
+      assertEquals(model.vocabSize(), tinyVocabSize);
+
+      // Check: topic summaries
+      Tuple2<int[], double[]>[] roundedTopicSummary = model.describeTopics();
+      assertEquals(roundedTopicSummary.length, k);
+      Tuple2<int[], double[]>[] roundedLocalTopicSummary = model.describeTopics();
+      assertEquals(roundedLocalTopicSummary.length, k);
+  }
+
   private static int tinyK = LDASuite$.MODULE$.tinyK();
   private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
   private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();