diff --git a/gtsam/discrete/DiscreteKey.h b/gtsam/discrete/DiscreteKey.h
index dea00074d..297d5570d 100644
--- a/gtsam/discrete/DiscreteKey.h
+++ b/gtsam/discrete/DiscreteKey.h
@@ -69,6 +69,16 @@ namespace gtsam {
       push_back(key);
       return *this;
     }
+
+    /// Print the keys and cardinalities.
+    void print(const std::string& s = "",
+               const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
+      for (auto&& dkey : *this) {
+        std::cout << DefaultKeyFormatter(dkey.first) << " " << dkey.second
+                  << std::endl;
+      }
+    }
+
   }; // DiscreteKeys
 
   /// Create a list from two keys
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.cpp b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
index 94e33890d..c031b9729 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
@@ -402,14 +402,35 @@ void HybridGaussianFactorGraph::add(DecisionTreeFactor::shared_ptr factor) {
 }
 
 /* ************************************************************************ */
-const Ordering HybridGaussianFactorGraph::getHybridOrdering(
-    OptionalOrderingType orderingType) const {
+const KeySet HybridGaussianFactorGraph::getDiscreteKeys() const {
   KeySet discrete_keys;
   for (auto &factor : factors_) {
     for (const DiscreteKey &k : factor->discreteKeys()) {
       discrete_keys.insert(k.first);
     }
   }
+  return discrete_keys;
+}
+
+/* ************************************************************************ */
+const KeySet HybridGaussianFactorGraph::getContinuousKeys() const {
+  KeySet keys;
+  for (auto &factor : factors_) {
+    for (const Key &key : factor->continuousKeys()) {
+      keys.insert(key);
+    }
+  }
+  return keys;
+}
+
+/* ************************************************************************ */
+const Ordering HybridGaussianFactorGraph::getHybridOrdering() const {
+  KeySet discrete_keys = getDiscreteKeys();
+  for (auto &factor : factors_) {
+    for (const DiscreteKey &k : factor->discreteKeys()) {
+      discrete_keys.insert(k.first);
+    }
+  }
 
   const VariableIndex index(factors_);
   Ordering ordering = Ordering::ColamdConstrainedLast(
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.h b/gtsam/hybrid/HybridGaussianFactorGraph.h
index 56f9b7e07..ad5cde09b 100644
--- a/gtsam/hybrid/HybridGaussianFactorGraph.h
+++ b/gtsam/hybrid/HybridGaussianFactorGraph.h
@@ -161,14 +161,19 @@ class GTSAM_EXPORT HybridGaussianFactorGraph
     }
   }
 
+  /// Get all the discrete keys in the factor graph.
+  const KeySet getDiscreteKeys() const;
+
+  /// Get all the continuous keys in the factor graph.
+  const KeySet getContinuousKeys() const;
+
   /**
-   * @brief 
-   * 
-   * @param orderingType 
-   * @return const Ordering 
+   * @brief Return a Colamd constrained ordering where the discrete keys are
+   * eliminated after the continuous keys.
+   *
+   * @return const Ordering
    */
-  const Ordering getHybridOrdering(
-      OptionalOrderingType orderingType = boost::none) const;
+  const Ordering getHybridOrdering() const;
 };
 
 }  // namespace gtsam
diff --git a/gtsam/hybrid/HybridValues.h b/gtsam/hybrid/HybridValues.h
index d5c78f951..4928f9384 100644
--- a/gtsam/hybrid/HybridValues.h
+++ b/gtsam/hybrid/HybridValues.h
@@ -31,8 +31,8 @@
 namespace gtsam {
 
 /**
- * HybridValues represents a collection of DiscreteValues and VectorValues.  It
- * is typically used to store the variables of a HybridGaussianFactorGraph.
+ * HybridValues represents a collection of DiscreteValues and VectorValues.
+ * It is typically used to store the variables of a HybridGaussianFactorGraph.
  * Optimizing a HybridGaussianBayesNet returns this class.
  */
 class GTSAM_EXPORT HybridValues {
@@ -47,10 +47,10 @@ class GTSAM_EXPORT HybridValues {
   /// @name Standard Constructors
   /// @{
 
-  // Default constructor creates an empty HybridValues.
+  /// Default constructor creates an empty HybridValues.
   HybridValues() = default;
 
-  // Construct from DiscreteValues and VectorValues.
+  /// Construct from DiscreteValues and VectorValues.
   HybridValues(const DiscreteValues& dv, const VectorValues& cv)
       : discrete_(dv), continuous_(cv){};
 
@@ -58,7 +58,7 @@ class GTSAM_EXPORT HybridValues {
   /// @name Testable
   /// @{
 
-  // print required by Testable for unit testing
+  /// print required by Testable for unit testing
   void print(const std::string& s = "HybridValues",
              const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
     std::cout << s << ": \n";
@@ -67,7 +67,7 @@ class GTSAM_EXPORT HybridValues {
                       keyFormatter);  // print continuous components
   };
 
-  // equals required by Testable for unit testing
+  /// equals required by Testable for unit testing
   bool equals(const HybridValues& other, double tol = 1e-9) const {
     return discrete_.equals(other.discrete_, tol) &&
            continuous_.equals(other.continuous_, tol);
@@ -83,13 +83,13 @@ class GTSAM_EXPORT HybridValues {
   /// Return the delta update for the continuous vectors
   VectorValues continuous() const { return continuous_; }
 
-  // Check whether a variable with key \c j exists in DiscreteValue.
+  /// Check whether a variable with key \c j exists in DiscreteValue.
   bool existsDiscrete(Key j) { return (discrete_.find(j) != discrete_.end()); };
 
-  // Check whether a variable with key \c j exists in VectorValue.
+  /// Check whether a variable with key \c j exists in VectorValue.
   bool existsVector(Key j) { return continuous_.exists(j); };
 
-  // Check whether a variable with key \c j exists.
+  /// Check whether a variable with key \c j exists.
   bool exists(Key j) { return existsDiscrete(j) || existsVector(j); };
 
   /** Insert a discrete \c value with key \c j.  Replaces the existing value if
diff --git a/gtsam/hybrid/hybrid.i b/gtsam/hybrid/hybrid.i
index dfbf3919d..86029a48a 100644
--- a/gtsam/hybrid/hybrid.i
+++ b/gtsam/hybrid/hybrid.i
@@ -99,6 +99,8 @@ class HybridBayesTree {
   bool empty() const;
   const HybridBayesTreeClique* operator[](size_t j) const;
 
+  gtsam::HybridValues optimize() const;
+
   string dot(const gtsam::KeyFormatter& keyFormatter =
                  gtsam::DefaultKeyFormatter) const;
 };
diff --git a/gtsam/hybrid/tests/testGaussianHybridFactorGraph.cpp b/gtsam/hybrid/tests/testGaussianHybridFactorGraph.cpp
index e4bd0e084..40da42412 100644
--- a/gtsam/hybrid/tests/testGaussianHybridFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testGaussianHybridFactorGraph.cpp
@@ -184,8 +184,8 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalSimple) {
   hfg.add(DecisionTreeFactor(m1, {2, 8}));
   hfg.add(DecisionTreeFactor({{M(1), 2}, {M(2), 2}}, "1 2 3 4"));
 
-  HybridBayesTree::shared_ptr result = hfg.eliminateMultifrontal(
-      Ordering::ColamdConstrainedLast(hfg, {M(1), M(2)}));
+  HybridBayesTree::shared_ptr result =
+      hfg.eliminateMultifrontal(hfg.getHybridOrdering());
 
   // The bayes tree should have 3 cliques
   EXPECT_LONGS_EQUAL(3, result->size());
@@ -215,7 +215,7 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalCLG) {
   hfg.add(HybridDiscreteFactor(DecisionTreeFactor(m, {2, 8})));
 
   // Get a constrained ordering keeping c1 last
-  auto ordering_full = Ordering::ColamdConstrainedLast(hfg, {M(1)});
+  auto ordering_full = hfg.getHybridOrdering();
 
   // Returns a Hybrid Bayes Tree with distribution P(x0|x1)P(x1|c1)P(c1)
   HybridBayesTree::shared_ptr hbt = hfg.eliminateMultifrontal(ordering_full);
@@ -484,8 +484,7 @@ TEST(HybridGaussianFactorGraph, SwitchingTwoVar) {
   }
   HybridBayesNet::shared_ptr hbn;
   HybridGaussianFactorGraph::shared_ptr remaining;
-  std::tie(hbn, remaining) =
-      hfg->eliminatePartialSequential(ordering_partial);
+  std::tie(hbn, remaining) = hfg->eliminatePartialSequential(ordering_partial);
 
   EXPECT_LONGS_EQUAL(14, hbn->size());
   EXPECT_LONGS_EQUAL(11, remaining->size());