diff --git a/examples/UnorderedLinear.cpp b/examples/UnorderedLinear.cpp
deleted file mode 100644
index f3d0014e4..000000000
--- a/examples/UnorderedLinear.cpp
+++ /dev/null
@@ -1,290 +0,0 @@
-
-#include <gtsam/slam/dataset.h>
-#include <gtsam/geometry/Pose2.h>
-#include <gtsam/inference/VariableIndex.h>
-#include <gtsam/linear/GaussianFactorGraph.h>
-#include <gtsam/linear/VectorValues.h>
-#include <gtsam/linear/GaussianMultifrontalSolver.h>
-#include <gtsam/linear/GaussianBayesTree.h>
-#include <gtsam/linear/GaussianJunctionTree.h>
-#include <gtsam/linear/GaussianEliminationTree.h>
-#include <gtsam/slam/PriorFactor.h>
-#include <gtsam/slam/BetweenFactor.h>
-#include <gtsam/nonlinear/Symbol.h>
-#include <gtsam/nonlinear/ISAM2.h>
-#include <string>
-
-#include <boost/timer/timer.hpp>
-#include <boost/archive/binary_oarchive.hpp>
-#include <boost/archive/binary_iarchive.hpp>
-#include <boost/serialization/export.hpp>
-
-using namespace gtsam;
-using namespace std;
-
-BOOST_CLASS_EXPORT(Value);
-BOOST_CLASS_EXPORT(Pose2);
-BOOST_CLASS_EXPORT(NonlinearFactor);
-BOOST_CLASS_EXPORT(NoiseModelFactor);
-BOOST_CLASS_EXPORT(NoiseModelFactor1<Pose2>);
-typedef NoiseModelFactor2<Pose2,Pose2> NMF2;
-BOOST_CLASS_EXPORT(NMF2);
-BOOST_CLASS_EXPORT(BetweenFactor<Pose2>);
-BOOST_CLASS_EXPORT(PriorFactor<Pose2>);
-BOOST_CLASS_EXPORT(noiseModel::Base);
-BOOST_CLASS_EXPORT(noiseModel::Isotropic);
-BOOST_CLASS_EXPORT(noiseModel::Gaussian);
-BOOST_CLASS_EXPORT(noiseModel::Diagonal);
-BOOST_CLASS_EXPORT(noiseModel::Unit);
-
-double chi2_red(const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& config) {
-  // Compute degrees of freedom (observations - variables)
-  // In ocaml, +1 was added to the observations to account for the prior, but
-  // the factor graph already includes a factor for the prior/equality constraint.
-  //  double dof = graph.size() - config.size();
-  int graph_dim = 0;
-  BOOST_FOREACH(const boost::shared_ptr<gtsam::NonlinearFactor>& nlf, graph) {
-    graph_dim += (int)nlf->dim();
-  }
-  double dof = double(graph_dim - config.dim()); // kaess: changed to dim
-  return 2. * graph.error(config) / dof; // kaess: added factor 2, graph.error returns half of actual error
-}
-
-/* ************************************************************************* */
-ISAM2 solveWithOldISAM2(const NonlinearFactorGraph& measurements)
-{
-  ISAM2 isam2;
-
-  size_t nextMeasurement = 0;
-  for(size_t step=1; nextMeasurement < measurements.size() && nextMeasurement < 1000; ++step) {
-
-    Values newVariables;
-    NonlinearFactorGraph newFactors;
-
-    // Collect measurements and new variables for the current step
-    gttic_(Collect_measurements);
-    if(step == 1) {
-      //      cout << "Initializing " << 0 << endl;
-      newVariables.insert(0, Pose2());
-      // Add prior
-      newFactors.add(PriorFactor<Pose2>(0, Pose2(), noiseModel::Unit::Create(Pose2::Dim())));
-    }
-    while(nextMeasurement < measurements.size()) {
-
-      NonlinearFactor::shared_ptr measurementf = measurements[nextMeasurement];
-
-      if(BetweenFactor<Pose2>::shared_ptr measurement =
-        boost::dynamic_pointer_cast<BetweenFactor<Pose2> >(measurementf))
-      {
-        // Stop collecting measurements that are for future steps
-        if(measurement->key1() > step || measurement->key2() > step)
-          break;
-
-        // Require that one of the nodes is the current one
-        if(measurement->key1() != step && measurement->key2() != step)
-          throw runtime_error("Problem in data file, out-of-sequence measurements");
-
-        // Add a new factor
-        newFactors.push_back(measurement);
-
-        // Initialize the new variable
-        if(measurement->key1() == step && measurement->key2() == step-1) {
-          if(step == 1)
-            newVariables.insert(step, measurement->measured().inverse());
-          else {
-            Pose2 prevPose = isam2.calculateEstimate<Pose2>(step-1);
-            newVariables.insert(step, prevPose * measurement->measured().inverse());
-          }
-          //        cout << "Initializing " << step << endl;
-        } else if(measurement->key2() == step && measurement->key1() == step-1) {
-          if(step == 1)
-            newVariables.insert(step, measurement->measured());
-          else {
-            Pose2 prevPose = isam2.calculateEstimate<Pose2>(step-1);
-            newVariables.insert(step, prevPose * measurement->measured());
-          }
-          //        cout << "Initializing " << step << endl;
-        }
-      } else {
-        throw std::runtime_error("Unknown factor type read from data file");
-      }
-      ++ nextMeasurement;
-    }
-    gttoc_(Collect_measurements);
-
-    // Update iSAM2
-    gttic_(Update_ISAM2);
-    isam2.update(newFactors, newVariables);
-    gttoc_(Update_ISAM2);
-
-    tictoc_finishedIteration_();
-
-    if(step % 1000 == 0) {
-      cout << "Step " << step << endl;
-      tictoc_print_();
-    }
-  }
-
-  return isam2;
-}
-
-/* ************************************************************************* */
-GaussianFactorGraph convertToUnordered(const GaussianFactorGraph& gfg, const Ordering& ordering)
-{
-  GaussianFactorGraph gfgu;
-  BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, gfg)
-  {
-    vector<std::pair<Key, Matrix> > terms;
-    
-    const JacobianFactor& jacobian = dynamic_cast<const JacobianFactor&>(*factor);
-    for(GaussianFactor::const_iterator term = jacobian.begin(); term != jacobian.end(); ++term)
-    {
-      terms.push_back(make_pair(
-        ordering.key(*term),
-        jacobian.getA(term)));
-    }
-
-    gfgu.add(JacobianFactor(terms, jacobian.getb(), jacobian.get_model()));
-  }
-  return gfgu;
-}
-
-/* ************************************************************************* */
-void compareSolutions(const VectorValues& orderedSoln, const Ordering& ordering, const VectorValues& unorderedSoln)
-{
-  if(orderedSoln.size() != unorderedSoln.size())
-  {
-    cout << "Solution sizes are not the same" << endl;
-  }
-  else
-  {
-    double maxErr = 0.0;
-    BOOST_FOREACH(const VectorValues::KeyValuePair& v, unorderedSoln)
-    {
-      Vector orderedV = orderedSoln[ordering[v.first]];
-      maxErr = std::max(maxErr, (orderedV - v.second).cwiseAbs().maxCoeff());
-    }
-    cout << "Maximum abs error: " << maxErr << endl;
-  }
-}
-
-/* ************************************************************************* */
-int main(int argc, char *argv[])
-{
-
-  //try {
-    //// Load graph
-    //gttic_(Find_datafile);
-    //string datasetFile = findExampleDataFile("w10000-odom");
-    //std::pair<NonlinearFactorGraph::shared_ptr, Values::shared_ptr> data = load2D(datasetFile);
-    //gttoc_(Find_datafile);
-
-    //NonlinearFactorGraph measurements = *data.first;
-    //Values initial = *data.second;
-
-    //// Solve with old ISAM2 to get initial estimate
-    //cout << "Solving with old ISAM2 to obtain initial estimate" << endl;
-  //  ISAM2 isam = solveWithOldISAM2(measurements);
-  //  {
-  //    std::ofstream writerStream("incremental_init1000", ios::binary);
-  //    boost::archive::binary_oarchive writer(writerStream);
-  //    writer << isam.calculateEstimate();
-  //    writerStream.close();
-  //  }
-  //  {
-  //    std::ofstream writerStream("incremental_graph1000", ios::binary);
-  //    boost::archive::binary_oarchive writer(writerStream);
-  //    writer << isam.getFactorsUnsafe();
-  //    writerStream.close();
-  //  }
-  //} catch(std::exception& e) {
-  //  cout << e.what() << endl;
-  //}
-
-  Values isamsoln;
-  NonlinearFactorGraph nlfg;
-
-  {
-    std::ifstream readerStream("incremental_init", ios::binary);
-    boost::archive::binary_iarchive reader(readerStream);
-    reader >> isamsoln;
-  }
-  {
-    std::ifstream readerStream("incremental_graph", ios::binary);
-    boost::archive::binary_iarchive reader(readerStream);
-    reader >> nlfg;
-  }
-
-  // Get linear graph
-  cout << "Converting to unordered linear graph" << endl;
-  Ordering ordering = *isamsoln.orderingArbitrary();
-  Ordering orderingCOLAMD = *nlfg.orderingCOLAMD(isamsoln);
-  GaussianFactorGraph gfg = *nlfg.linearize(isamsoln, ordering);
-  GaussianFactorGraph gfgu = convertToUnordered(gfg, ordering);
-
-  //Ordering orderingUnordered;
-  //for(Index j = 0; j < ordering.size(); ++j)
-  //  orderingUnordered.push_back(ordering.key(j));
-
-  // Solve linear graph
-  cout << "Optimizing unordered graph" << endl;
-  VectorValues unorderedSolnFinal;
-  {
-    gttic_(Solve_unordered);
-    VectorValues unorderedSoln;
-    for(size_t i = 0; i < 1; ++i) {
-      gttic_(VariableIndex);
-      VariableIndex vi(gfgu);
-      gttoc_(VariableIndex);
-      gttic_(COLAMD);
-      Ordering orderingUnordered = Ordering::COLAMD(vi);
-      gttoc_(COLAMD);
-      gttic_(eliminate);
-      GaussianBayesTree::shared_ptr bt = gfgu.eliminateMultifrontal(orderingUnordered);
-      gttoc_(eliminate);
-      gttic_(optimize);
-      unorderedSoln = bt->optimize();
-      gttoc_(optimize);
-    }
-    gttoc_(Solve_unordered);
-    unorderedSolnFinal = unorderedSoln;
-  }
-
-  // Solve linear graph with old code
-  cout << "Optimizing using old ordered code" << endl;
-  VectorValues orderedSolnFinal;
-  {
-    Ordering orderingToUse = ordering;
-    GaussianFactorGraph::shared_ptr orderedGraph = nlfg.linearize(isamsoln, *nlfg.orderingCOLAMD(isamsoln));
-    gttic_(Solve_ordered);
-    VectorValues orderedSoln;
-    for(size_t i = 0; i < 1; ++i) {
-      gttic_(VariableIndex);
-      boost::shared_ptr<VariableIndex> vi = boost::make_shared<VariableIndex>(gfg);
-      gttoc_(VariableIndex);
-      gttic_(COLAMD);
-      boost::shared_ptr<Permutation> permutation = inference::PermutationCOLAMD(*vi);
-      orderingToUse.permuteInPlace(*permutation);
-      gttoc_(COLAMD);
-      gttic_(eliminate);
-      boost::shared_ptr<GaussianBayesTree> bt = GaussianMultifrontalSolver(*orderedGraph, true).eliminate();
-      gttoc_(eliminate);
-      gttic_(optimize);
-      orderedSoln = optimize(*bt);
-      gttoc_(optimize);
-    }
-    gttoc_(Solve_ordered);
-    orderedSolnFinal = orderedSoln;
-  }
-
-  // Compare results
-  compareSolutions(orderedSolnFinal, orderingCOLAMD, unorderedSolnFinal);
-
-  //GaussianEliminationTree(gfgu, orderingUnordered).print("ETree: ");
-  //GaussianJunctionTree(GaussianEliminationTree(gfgu, Ordering::COLAMD(gfgu))).print("JTree: ");
-  //gfgu.eliminateMultifrontal(orderingUnordered)->print("BayesTree: ");
-
-  tictoc_print_();
-
-  return 0;
-}
\ No newline at end of file