diff --git a/gtsam/nonlinear/NonlinearFactor.h b/gtsam/nonlinear/NonlinearFactor.h
index d2c3e20fa..4d26bcc63 100644
--- a/gtsam/nonlinear/NonlinearFactor.h
+++ b/gtsam/nonlinear/NonlinearFactor.h
@@ -356,12 +356,8 @@ namespace gtsam {
 			this->noiseModel_->WhitenInPlace(A1);
 			this->noiseModel_->WhitenInPlace(A2);
 			this->noiseModel_->whitenInPlace(b);
-			if(var1 < var2)
-			  return GaussianFactor::shared_ptr(new JacobianFactor(var1, A1, var2,
-			      A2, b, noiseModel::Unit::Create(b.size())));
-			else
-			  return GaussianFactor::shared_ptr(new JacobianFactor(var2, A2, var1,
-			      A1, b, noiseModel::Unit::Create(b.size())));
+			return GaussianFactor::shared_ptr(new JacobianFactor(var1, A1, var2,
+			    A2, b, noiseModel::Unit::Create(b.size())));
 		}
 
 		/**
@@ -370,10 +366,7 @@ namespace gtsam {
      */
     virtual IndexFactor::shared_ptr symbolic(const Ordering& ordering) const {
       const Index var1 = ordering[key1_], var2 = ordering[key2_];
-      if(var1 < var2)
-        return IndexFactor::shared_ptr(new IndexFactor(var1, var2));
-      else
-        return IndexFactor::shared_ptr(new IndexFactor(var2, var1));
+      return IndexFactor::shared_ptr(new IndexFactor(var1, var2));
     }
 
 		/** methods to retrieve both keys */
@@ -502,28 +495,8 @@ namespace gtsam {
       this->noiseModel_->WhitenInPlace(A2);
       this->noiseModel_->WhitenInPlace(A3);
       this->noiseModel_->whitenInPlace(b);
-      if(var1 < var2 && var2 < var3)
-        return GaussianFactor::shared_ptr(
-            new JacobianFactor(var1, A1, var2, A2, var3, A3, b, noiseModel::Unit::Create(b.size())));
-      else if(var2 < var1 && var1 < var3)
-        return GaussianFactor::shared_ptr(
-            new JacobianFactor(var2, A2, var1, A1, var3, A3, b, noiseModel::Unit::Create(b.size())));
-      else if(var1 < var3 && var3 < var2)
-        return GaussianFactor::shared_ptr(
-            new JacobianFactor(var1, A1, var3, A3, var2, A2, b, noiseModel::Unit::Create(b.size())));
-      else if(var2 < var3 && var3 < var1)
-        return GaussianFactor::shared_ptr(
-            new JacobianFactor(var2, A2, var3, A3, var1, A1, b, noiseModel::Unit::Create(b.size())));
-      else if(var3 < var1 && var1 < var2)
-        return GaussianFactor::shared_ptr(
-            new JacobianFactor(var3, A3, var1, A1, var2, A2, b, noiseModel::Unit::Create(b.size())));
-      else if(var3 < var2 && var2 < var1)
-        return GaussianFactor::shared_ptr(
-            new JacobianFactor(var3, A3, var2, A2, var1, A1, b, noiseModel::Unit::Create(b.size())));
-      else {
-        assert(false);
-        return GaussianFactor::shared_ptr();
-      }
+      return GaussianFactor::shared_ptr(
+          new JacobianFactor(var1, A1, var2, A2, var3, A3, b, noiseModel::Unit::Create(b.size())));
     }
 
     /**
@@ -532,22 +505,7 @@ namespace gtsam {
      */
     virtual IndexFactor::shared_ptr symbolic(const Ordering& ordering) const {
       const Index var1 = ordering[key1_], var2 = ordering[key2_], var3 = ordering[key3_];
-      if(var1 < var2 && var2 < var3)
-        return IndexFactor::shared_ptr(new IndexFactor(ordering[key1_], ordering[key2_], ordering[key3_]));
-      else if(var2 < var1 && var1 < var3)
-        return IndexFactor::shared_ptr(new IndexFactor(ordering[key2_], ordering[key1_], ordering[key3_]));
-      else if(var1 < var3 && var3 < var2)
-        return IndexFactor::shared_ptr(new IndexFactor(ordering[key1_], ordering[key3_], ordering[key2_]));
-      else if(var2 < var3 && var3 < var1)
-        return IndexFactor::shared_ptr(new IndexFactor(ordering[key2_], ordering[key3_], ordering[key1_]));
-      else if(var3 < var1 && var1 < var2)
-        return IndexFactor::shared_ptr(new IndexFactor(ordering[key3_], ordering[key1_], ordering[key2_]));
-      else if(var3 < var2 && var2 < var1)
-        return IndexFactor::shared_ptr(new IndexFactor(ordering[key3_], ordering[key2_], ordering[key1_]));
-      else {
-        assert(false);
-        return GaussianFactor::shared_ptr();
-      }
+      return IndexFactor::shared_ptr(new IndexFactor(ordering[key1_], ordering[key2_], ordering[key3_]));
     }
 
     /** methods to retrieve keys */
diff --git a/gtsam/slam/smallExample.cpp b/gtsam/slam/smallExample.cpp
index 17b3342b3..c387ed469 100644
--- a/gtsam/slam/smallExample.cpp
+++ b/gtsam/slam/smallExample.cpp
@@ -144,22 +144,13 @@ namespace example {
 		fg.add(ordering["x1"], 10*eye(2), -1.0*ones(2), unit2);
 
 		// odometry between x1 and x2: x2-x1=[0.2;-0.1]
-		if(ordering["x1"] < ordering["x2"])
-		  fg.add(ordering["x1"], -10*eye(2),ordering["x2"], 10*eye(2), Vector_(2, 2.0, -1.0), unit2);
-		else
-		  fg.add(ordering["x2"], 10*eye(2),ordering["x1"], -10*eye(2), Vector_(2, 2.0, -1.0), unit2);
+		fg.add(ordering["x1"], -10*eye(2),ordering["x2"], 10*eye(2), Vector_(2, 2.0, -1.0), unit2);
 
     // measurement between x1 and l1: l1-x1=[0.0;0.2]
-		if(ordering["x1"] < ordering["l1"])
-		  fg.add(ordering["x1"], -5*eye(2), ordering["l1"], 5*eye(2), Vector_(2, 0.0, 1.0), unit2);
-		else
-		  fg.add(ordering["l1"], 5*eye(2), ordering["x1"], -5*eye(2), Vector_(2, 0.0, 1.0), unit2);
+		fg.add(ordering["x1"], -5*eye(2), ordering["l1"], 5*eye(2), Vector_(2, 0.0, 1.0), unit2);
 
 		// measurement between x2 and l1: l1-x2=[-0.2;0.3]
-		if(ordering["x2"] < ordering["l1"])
-		  fg.add(ordering["x2"], -5*eye(2), ordering["l1"], 5*eye(2), Vector_(2, -1.0, 1.5), unit2);
-		else
-		  fg.add(ordering["l1"], 5*eye(2), ordering["x2"], -5*eye(2), Vector_(2, -1.0, 1.5), unit2);
+		fg.add(ordering["x2"], -5*eye(2), ordering["l1"], 5*eye(2), Vector_(2, -1.0, 1.5), unit2);
 
 		return *fg.dynamicCastFactors<FactorGraph<JacobianFactor> >();
 	}
diff --git a/tests/testSymbolicFactorGraph.cpp b/tests/testSymbolicFactorGraph.cpp
index eb951fc16..3c9a5667a 100644
--- a/tests/testSymbolicFactorGraph.cpp
+++ b/tests/testSymbolicFactorGraph.cpp
@@ -42,7 +42,7 @@ TEST( SymbolicFactorGraph, symbolicFactorGraph )
 	expected.push_factor(o["x1"]);
 	expected.push_factor(o["x1"],o["x2"]);
 	expected.push_factor(o["x1"],o["l1"]);
-	expected.push_factor(o["l1"],o["x2"]);
+	expected.push_factor(o["x2"],o["l1"]);
 
 	// construct it from the factor graph
 	GaussianFactorGraph factorGraph = createGaussianFactorGraph(o);