diff --git a/cpp/GaussianFactor.cpp b/cpp/GaussianFactor.cpp
index 65cb1ca04..03f199355 100644
--- a/cpp/GaussianFactor.cpp
+++ b/cpp/GaussianFactor.cpp
@@ -382,10 +382,9 @@ void GaussianFactor::append_factor(GaussianFactor::shared_ptr f, size_t m, size_
 
 pair<GaussianConditional::shared_ptr, GaussianFactor::shared_ptr>
 GaussianFactor::eliminateMatrix(Matrix& Ab, SharedDiagonal model,
-		        const Ordering& ordering,
+		        const Ordering& frontal, const Ordering& separator,
 		        const Dimensions& dimensions) {
 	bool verbose = false;
-	Symbol key = ordering.front();
 
 	// Use in-place QR on dense Ab appropriate to NoiseModel
 	if (verbose) model->print("Before QR");
@@ -394,15 +393,15 @@ GaussianFactor::eliminateMatrix(Matrix& Ab, SharedDiagonal model,
 
 	// get dimensions of the eliminated variable
 	// TODO: this is another map find that should be avoided !
-	size_t n1 = dimensions.at(key), n = Ab.size2() - 1;
+	size_t n1 = dimensions.at(frontal.front()), n = Ab.size2() - 1;
 
 	// if m<n1, this factor cannot be eliminated
 	size_t maxRank = noiseModel->dim();
 	if (maxRank<n1) {
 		cout << "Perhaps your factor graph is singular." << endl;
 		cout << "Here are the keys involved in the factor now being eliminated:" << endl;
-		ordering.print("Keys");
-		cout << "The first key, '" << (string)ordering.front() << "', corresponds to the variable being eliminated" << endl;
+		separator.print("Keys");
+		cout << "The first key, '" << (string)frontal.front() << "', corresponds to the variable being eliminated" << endl;
 		throw(domain_error("GaussianFactor::eliminate: fewer constraints than unknowns"));
 	}
 
@@ -410,7 +409,7 @@ GaussianFactor::eliminateMatrix(Matrix& Ab, SharedDiagonal model,
 	ublas::matrix_column<Matrix> d(Ab,n);
 
 	// create base conditional Gaussian
-	GaussianConditional::shared_ptr conditional(new GaussianConditional(key,
+	GaussianConditional::shared_ptr conditional(new GaussianConditional(frontal.front(),
 			sub(d,  0, n1),                   // form d vector
 			sub(Ab, 0, n1, 0, n1),            // form R matrix
 			sub(noiseModel->sigmas(),0,n1))); // get standard deviations
@@ -418,8 +417,8 @@ GaussianFactor::eliminateMatrix(Matrix& Ab, SharedDiagonal model,
 	// extract the block matrices for parents in both CG and LF
 	GaussianFactor::shared_ptr factor(new GaussianFactor);
 	size_t j = n1;
-	BOOST_FOREACH(const Symbol& cur_key, ordering)
-		if (cur_key!=key) {
+	BOOST_FOREACH(const Symbol& cur_key, separator)
+		if (cur_key!=frontal.front()) {
 			size_t dim = dimensions.at(cur_key); // TODO avoid find !
 			conditional->add(cur_key, sub(Ab, 0, n1, j, j+dim));
 			factor->insert(cur_key, sub(Ab, n1, maxRank, j, j+dim)); // TODO: handle zeros properly
@@ -454,7 +453,8 @@ GaussianFactor::eliminate(const Symbol& key) const
 	}
 
 	// create an internal ordering that eliminates key first
-	Ordering ordering;
+	Ordering frontal, ordering;
+	frontal += key;
 	ordering += key;
 	BOOST_FOREACH(const Symbol& k, keys())
 		if (k != key) ordering += k;
@@ -463,7 +463,8 @@ GaussianFactor::eliminate(const Symbol& key) const
 	Matrix Ab = matrix_augmented(ordering,false);
 
 	// TODO: this is where to split
-	return eliminateMatrix(Ab, model_, ordering, dimensions());
+	ordering.pop_front();
+	return eliminateMatrix(Ab, model_, frontal, ordering, dimensions());
 }
 
 /* ************************************************************************* */
diff --git a/cpp/GaussianFactor.h b/cpp/GaussianFactor.h
index cd6d4ffac..6142e10c2 100644
--- a/cpp/GaussianFactor.h
+++ b/cpp/GaussianFactor.h
@@ -248,8 +248,8 @@ public:
 	 */
 	static std::pair<boost::shared_ptr<GaussianConditional>, shared_ptr>
 	eliminateMatrix(Matrix& Ab, SharedDiagonal model,
-			        const Ordering& ordering,
-			        const Dimensions& dimensions);
+			const Ordering& frontal, const Ordering& separator,
+			const Dimensions& dimensions);
 
 	/**
 	 * Take the factor f, and append to current matrices. Not very general.
diff --git a/cpp/GaussianFactorGraph.cpp b/cpp/GaussianFactorGraph.cpp
index ab1382de8..847d32bb6 100644
--- a/cpp/GaussianFactorGraph.cpp
+++ b/cpp/GaussianFactorGraph.cpp
@@ -223,7 +223,7 @@ GaussianFactorGraph::eliminateOneMatrixJoin(const Symbol& key) {
 	}
 
 	// add the keys to the rendering
-	Ordering render; render += key;
+	Ordering frontal, render; frontal += key; render += key;
 	BOOST_FOREACH(const Symbol& k, separator)
 			if (k != key) render += k;
 
@@ -235,8 +235,9 @@ GaussianFactorGraph::eliminateOneMatrixJoin(const Symbol& key) {
 	// eliminate that joint factor
 	GaussianFactor::shared_ptr factor;
 	GaussianConditional::shared_ptr conditional;
+	render.pop_front();
 	boost::tie(conditional, factor) =
-			GaussianFactor::eliminateMatrix(Ab, model, render, dimensions);
+			GaussianFactor::eliminateMatrix(Ab, model, frontal, render, dimensions);
 
 	// add new factor on separator back into the graph
 	if (!factor->empty()) push_back(factor);
diff --git a/cpp/testJunctionTree.cpp b/cpp/testJunctionTree.cpp
index 5c67af9d0..c8d148b17 100644
--- a/cpp/testJunctionTree.cpp
+++ b/cpp/testJunctionTree.cpp
@@ -62,7 +62,7 @@ TEST( GaussianFactorGraph, constructor )
 }
 
 
-/* ************************************************************************* *
+/* ************************************************************************* */
 TEST( GaussianFactorGraph, optimizeMultiFrontal )
 {
 	// create a graph
@@ -72,13 +72,13 @@ TEST( GaussianFactorGraph, optimizeMultiFrontal )
 	Ordering ordering; ordering += "x1","x3","x5","x7","x2","x6","x4";
 
 	// optimize the graph
-	LinearJunctionTree<GaussianConditional, GaussianFactorGraph> junctionTree(fg, ordering);
-	VectorConfig actual = junctionTree.optimize();
-
-	// verify
-	VectorConfig expected = createCorrectDelta();
-
-  CHECK(assert_equal(expected,actual));
+//	GaussianJunctionTree<GaussianFactorGraph> junctionTree(fg, ordering);
+//	VectorConfig actual = junctionTree.optimize();
+//
+//	// verify
+//	VectorConfig expected = createCorrectDelta();
+//
+//  CHECK(assert_equal(expected,actual));
 }
 
 /* ************************************************************************* */