Get rid of all (pre c++11) += calls to create Orderings.

examples/DiscreteBayesNetExample.cpp

@@ -51,8 +51,7 @@ int main(int argc, char **argv) {
   DiscreteFactorGraph fg(asia);
 
   // Create solver and eliminate
-  Ordering ordering;
-  ordering += Key(0), Key(1), Key(2), Key(3), Key(4), Key(5), Key(6), Key(7);
+  const Ordering ordering{0, 1, 2, 3, 4, 5, 6, 7};
 
   // solve
   auto mpe = fg.optimize();

gtsam/discrete/tests/testDiscreteBayesNet.cpp

@@ -94,8 +94,7 @@ TEST(DiscreteBayesNet, Asia) {
   EXPECT(assert_equal(vs, marginals.marginalProbabilities(Smoking)));
 
   // Create solver and eliminate
-  Ordering ordering;
-  ordering += Key(0), Key(1), Key(2), Key(3), Key(4), Key(5), Key(6), Key(7);
+  const Ordering ordering{0, 1, 2, 3, 4, 5, 6, 7};
   DiscreteBayesNet::shared_ptr chordal = fg.eliminateSequential(ordering);
   DiscreteConditional expected2(Bronchitis % "11/9");
   EXPECT(assert_equal(expected2, *chordal->back()));

gtsam/discrete/tests/testDiscreteFactorGraph.cpp

@@ -107,8 +107,7 @@ TEST(DiscreteFactorGraph, test) {
   graph.add(C & B, "3 1 1 3");
 
   // Test EliminateDiscrete
-  Ordering frontalKeys;
-  frontalKeys += Key(0);
+  const Ordering frontalKeys{0};
   const auto [conditional, newFactor] = EliminateDiscrete(graph, frontalKeys);
 
   // Check Conditional
@@ -123,8 +122,7 @@ TEST(DiscreteFactorGraph, test) {
   EXPECT(assert_equal(expectedFactor, *newFactor));
 
   // Test using elimination tree
-  Ordering ordering;
-  ordering += Key(0), Key(1), Key(2);
+  const Ordering ordering{0, 1, 2};
   DiscreteEliminationTree etree(graph, ordering);
   const auto [actual, remainingGraph] = etree.eliminate(&EliminateDiscrete);
 
@@ -231,8 +229,7 @@ TEST(DiscreteFactorGraph, testMPE_Darwiche09book_p244) {
   EXPECT(assert_equal(mpe, actualMPE));
 
   // Check Bayes Net
-  Ordering ordering;
-  ordering += Key(0), Key(1), Key(2), Key(3), Key(4);
+  const Ordering ordering{0, 1, 2, 3, 4};
   auto chordal = graph.eliminateSequential(ordering);
   EXPECT_LONGS_EQUAL(5, chordal->size());
 

gtsam/discrete/tests/testSignatureParser.cpp

@@ -91,7 +91,7 @@ TEST(SimpleParser, GibberishInMiddle) {
 
 // A test with slash in the end
 TEST(SimpleParser, SlashInEnd) {
-  const auto table = SignatureParser::parse("1/1 2/");
+  const auto table = SignatureParser::Parse("1/1 2/");
   EXPECT(!table);
 }
 

gtsam/hybrid/tests/testHybridBayesTree.cpp

@@ -97,7 +97,7 @@ TEST(HybridBayesTree, OptimizeAssignment) {
 
   // Create ordering.
   Ordering ordering;
-  for (size_t k = 0; k < s.K; k++) ordering += X(k);
+  for (size_t k = 0; k < s.K; k++) ordering.push_back(X(k));
 
   const auto [hybridBayesNet, remainingFactorGraph] =
       s.linearizedFactorGraph.eliminatePartialSequential(ordering);
@@ -139,7 +139,7 @@ TEST(HybridBayesTree, Optimize) {
 
   // Create ordering.
   Ordering ordering;
-  for (size_t k = 0; k < s.K; k++) ordering += X(k);
+  for (size_t k = 0; k < s.K; k++) ordering.push_back(X(k));
 
   const auto [hybridBayesNet, remainingFactorGraph] =
       s.linearizedFactorGraph.eliminatePartialSequential(ordering);

gtsam/hybrid/tests/testHybridEstimation.cpp

@@ -83,10 +83,10 @@ TEST(HybridEstimation, Full) {
 
   Ordering hybridOrdering;
   for (size_t k = 0; k < K; k++) {
-    hybridOrdering += X(k);
+    hybridOrdering.push_back(X(k));
   }
   for (size_t k = 0; k < K - 1; k++) {
-    hybridOrdering += M(k);
+    hybridOrdering.push_back(M(k));
   }
 
   HybridBayesNet::shared_ptr bayesNet =
@@ -442,8 +442,7 @@ TEST(HybridEstimation, eliminateSequentialRegression) {
   DiscreteConditional expected(m % "0.51341712/1");  // regression
 
   // Eliminate into BN using one ordering
-  Ordering ordering1;
-  ordering1 += X(0), X(1), M(0);
+  const Ordering ordering1{X(0), X(1), M(0)};
   HybridBayesNet::shared_ptr bn1 = fg->eliminateSequential(ordering1);
 
   // Check that the discrete conditional matches the expected.
@@ -451,8 +450,7 @@ TEST(HybridEstimation, eliminateSequentialRegression) {
   EXPECT(assert_equal(expected, *dc1, 1e-9));
 
   // Eliminate into BN using a different ordering
-  Ordering ordering2;
-  ordering2 += X(0), X(1), M(0);
+  const Ordering ordering2{X(0), X(1), M(0)};
   HybridBayesNet::shared_ptr bn2 = fg->eliminateSequential(ordering2);
 
   // Check that the discrete conditional matches the expected.

gtsam/hybrid/tests/testHybridGaussianISAM.cpp

@@ -126,10 +126,7 @@ TEST(HybridGaussianElimination, IncrementalInference) {
 
   /********************************************************/
   // Run batch elimination so we can compare results.
-  Ordering ordering;
-  ordering += X(0);
-  ordering += X(1);
-  ordering += X(2);
+  const Ordering ordering {X(0), X(1), X(2)};
 
   // Now we calculate the expected factors using full elimination
   const auto [expectedHybridBayesTree, expectedRemainingGraph] =
@@ -158,12 +155,10 @@ TEST(HybridGaussianElimination, IncrementalInference) {
 
   // We only perform manual continuous elimination for 0,0.
   // The other discrete probabilities on M(2) are calculated the same way
-  Ordering discrete_ordering;
-  discrete_ordering += M(0);
-  discrete_ordering += M(1);
+  const Ordering discreteOrdering{M(0), M(1)};
   HybridBayesTree::shared_ptr discreteBayesTree =
       expectedRemainingGraph->BaseEliminateable::eliminateMultifrontal(
-          discrete_ordering);
+          discreteOrdering);
 
   DiscreteValues m00;
   m00[M(0)] = 0, m00[M(1)] = 0;
@@ -225,7 +220,7 @@ TEST(HybridGaussianElimination, Approx_inference) {
   // Create ordering.
   Ordering ordering;
   for (size_t j = 0; j < 4; j++) {
-    ordering += X(j);
+    ordering.push_back(X(j));
   }
 
   // Now we calculate the actual factors using full elimination

gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp

@@ -265,7 +265,7 @@ TEST(HybridFactorGraph, EliminationTree) {
 
   // Create ordering.
   Ordering ordering;
-  for (size_t k = 0; k < self.K; k++) ordering += X(k);
+  for (size_t k = 0; k < self.K; k++) ordering.push_back(X(k));
 
   // Create elimination tree.
   HybridEliminationTree etree(self.linearizedFactorGraph, ordering);
@@ -286,8 +286,7 @@ TEST(GaussianElimination, Eliminate_x0) {
   factors.push_back(self.linearizedFactorGraph[1]);
 
   // Eliminate x0
-  Ordering ordering;
-  ordering += X(0);
+  const Ordering ordering{X(0)};
 
   auto result = EliminateHybrid(factors, ordering);
   CHECK(result.first);
@@ -310,8 +309,7 @@ TEST(HybridsGaussianElimination, Eliminate_x1) {
   factors.push_back(self.linearizedFactorGraph[2]);  // involves m1
 
   // Eliminate x1
-  Ordering ordering;
-  ordering += X(1);
+  const Ordering ordering{X(1)};
 
   auto result = EliminateHybrid(factors, ordering);
   CHECK(result.first);
@@ -346,9 +344,7 @@ TEST(HybridGaussianElimination, EliminateHybrid_2_Variable) {
   auto factors = self.linearizedFactorGraph;
 
   // Eliminate x0
-  Ordering ordering;
-  ordering += X(0);
-  ordering += X(1);
+  const Ordering ordering{X(0), X(1)};
 
   const auto [hybridConditionalMixture, factorOnModes] =
       EliminateHybrid(factors, ordering);
@@ -379,7 +375,7 @@ TEST(HybridFactorGraph, Partial_Elimination) {
 
   // Create ordering of only continuous variables.
   Ordering ordering;
-  for (size_t k = 0; k < self.K; k++) ordering += X(k);
+  for (size_t k = 0; k < self.K; k++) ordering.push_back(X(k));
 
   // Eliminate partially i.e. only continuous part.
   const auto [hybridBayesNet, remainingFactorGraph] =
@@ -415,7 +411,7 @@ TEST(HybridFactorGraph, Full_Elimination) {
   {
     // Create ordering.
     Ordering ordering;
-    for (size_t k = 0; k < self.K; k++) ordering += X(k);
+    for (size_t k = 0; k < self.K; k++) ordering.push_back(X(k));
 
     // Eliminate partially.
     const auto [hybridBayesNet_partial, remainingFactorGraph_partial] =
@@ -430,15 +426,15 @@ TEST(HybridFactorGraph, Full_Elimination) {
     }
 
     ordering.clear();
-    for (size_t k = 0; k < self.K - 1; k++) ordering += M(k);
+    for (size_t k = 0; k < self.K - 1; k++) ordering.push_back(M(k));
     discreteBayesNet =
         *discrete_fg.eliminateSequential(ordering, EliminateDiscrete);
   }
 
   // Create ordering.
   Ordering ordering;
-  for (size_t k = 0; k < self.K; k++) ordering += X(k);
-  for (size_t k = 0; k < self.K - 1; k++) ordering += M(k);
+  for (size_t k = 0; k < self.K; k++) ordering.push_back(X(k));
+  for (size_t k = 0; k < self.K - 1; k++) ordering.push_back(M(k));
 
   // Eliminate partially.
   HybridBayesNet::shared_ptr hybridBayesNet =
@@ -479,7 +475,7 @@ TEST(HybridFactorGraph, Printing) {
 
   // Create ordering.
   Ordering ordering;
-  for (size_t k = 0; k < self.K; k++) ordering += X(k);
+  for (size_t k = 0; k < self.K; k++) ordering.push_back(X(k));
 
   // Eliminate partially.
   const auto [hybridBayesNet, remainingFactorGraph] =
@@ -705,11 +701,7 @@ TEST(HybridFactorGraph, DefaultDecisionTree) {
   initialEstimate.insert(L(1), Point2(4.1, 1.8));
 
   // We want to eliminate variables not connected to DCFactors first.
-  Ordering ordering;
-  ordering += L(0);
-  ordering += L(1);
-  ordering += X(0);
-  ordering += X(1);
+  const Ordering ordering {L(0), L(1), X(0), X(1)};
 
   HybridGaussianFactorGraph linearized = *fg.linearize(initialEstimate);
 

gtsam/hybrid/tests/testHybridNonlinearISAM.cpp

@@ -143,10 +143,7 @@ TEST(HybridNonlinearISAM, IncrementalInference) {
 
   /********************************************************/
   // Run batch elimination so we can compare results.
-  Ordering ordering;
-  ordering += X(0);
-  ordering += X(1);
-  ordering += X(2);
+  const Ordering ordering {X(0), X(1), X(2)};
 
   // Now we calculate the actual factors using full elimination
   const auto [expectedHybridBayesTree, expectedRemainingGraph] =
@@ -176,12 +173,10 @@ TEST(HybridNonlinearISAM, IncrementalInference) {
 
   // We only perform manual continuous elimination for 0,0.
   // The other discrete probabilities on M(1) are calculated the same way
-  Ordering discrete_ordering;
-  discrete_ordering += M(0);
-  discrete_ordering += M(1);
+  const Ordering discreteOrdering{M(0), M(1)};
   HybridBayesTree::shared_ptr discreteBayesTree =
       expectedRemainingGraph->BaseEliminateable::eliminateMultifrontal(
-          discrete_ordering);
+          discreteOrdering);
 
   DiscreteValues m00;
   m00[M(0)] = 0, m00[M(1)] = 0;
@@ -244,7 +239,7 @@ TEST(HybridNonlinearISAM, Approx_inference) {
   // Create ordering.
   Ordering ordering;
   for (size_t j = 0; j < 4; j++) {
-    ordering += X(j);
+    ordering.push_back(X(j));
   }
 
   // Now we calculate the actual factors using full elimination

gtsam/inference/Ordering.h

@@ -66,15 +66,6 @@ public:
     return boost::assign::make_list_inserter(
         boost::assign_detail::call_push_back<This>(*this))(key);
   }
-#else
-  /** A simple inserter to insert one key at a time
-   * @param key The key to insert
-   * @return The ordering
-   */
-  This& operator+=(Key key) {
-    push_back(key);
-    return *this;
-  }
 #endif
 
   /**

gtsam/linear/tests/testGaussianBayesNet.cpp

@@ -211,8 +211,7 @@ TEST(GaussianBayesNet, MonteCarloIntegration) {
 /* ************************************************************************* */
 TEST(GaussianBayesNet, ordering)
 {
-  Ordering expected;
-  expected += _x_, _y_;
+  const Ordering expected{_x_, _y_};
   const auto actual = noisyBayesNet.ordering();
   EXPECT(assert_equal(expected, actual));
 }

gtsam/nonlinear/ExtendedKalmanFilter-inl.h

@@ -35,8 +35,7 @@ namespace gtsam {
     // Compute the marginal on the last key
     // Solve the linear factor graph, converting it into a linear Bayes Network
     // P(x0,x1) = P(x0|x1)*P(x1)
-    Ordering lastKeyAsOrdering;
-    lastKeyAsOrdering += lastKey;
+    const Ordering lastKeyAsOrdering{lastKey};
     const GaussianConditional::shared_ptr marginal =
       linearFactorGraph.marginalMultifrontalBayesNet(lastKeyAsOrdering)->front();
 

gtsam/symbolic/tests/testSymbolicFactorGraph.cpp

@@ -80,8 +80,7 @@ TEST(SymbolicFactorGraph, eliminatePartialSequential) {
 
 /* ************************************************************************* */
 TEST(SymbolicFactorGraph, eliminateFullMultifrontal) {
-  Ordering ordering;
-  ordering += 0, 1, 2, 3;
+  Ordering ordering{0, 1, 2, 3};
   SymbolicBayesTree actual1 = *simpleChain.eliminateMultifrontal(ordering);
   EXPECT(assert_equal(simpleChainBayesTree, actual1));
 
@@ -223,8 +222,7 @@ TEST(SymbolicFactorGraph, eliminate_disconnected_graph) {
   expected.emplace_shared<SymbolicConditional>(3, 4);
   expected.emplace_shared<SymbolicConditional>(4);
 
-  Ordering order;
-  order += 0, 1, 2, 3, 4;
+  const Ordering order{0, 1, 2, 3, 4};
   SymbolicBayesNet actual = *fg.eliminateSequential(order);
 
   EXPECT(assert_equal(expected, actual));

gtsam/symbolic/tests/testSymbolicJunctionTree.cpp

@@ -35,7 +35,7 @@ using namespace std;
  ****************************************************************************/
 TEST( JunctionTree, constructor )
 {
-  Ordering order; order += 0, 1, 2, 3;
+  const Ordering order{0, 1, 2, 3};
 
   SymbolicJunctionTree actual(SymbolicEliminationTree(simpleChain, order));
 

gtsam_unstable/discrete/Scheduler.cpp

@@ -248,7 +248,7 @@ DiscreteBayesNet::shared_ptr Scheduler::eliminate() const {
   // TODO: fix this!!
   size_t maxKey = keys().size();
   Ordering defaultKeyOrdering;
-  for (size_t i = 0; i < maxKey; ++i) defaultKeyOrdering += Key(i);
+  for (size_t i = 0; i < maxKey; ++i) defaultKeyOrdering.push_back(i);
   DiscreteBayesNet::shared_ptr chordal =
       this->eliminateSequential(defaultKeyOrdering);
   gttoc(my_eliminate);

gtsam_unstable/discrete/tests/testCSP.cpp

@@ -173,9 +173,7 @@ TEST(CSP, WesternUS) {
                      {6, 3}, {7, 2}, {8, 0}, {9, 1}, {10, 0}};
 
   // Create ordering according to example in ND-CSP.lyx
-  Ordering ordering;
-  ordering += Key(0), Key(1), Key(2), Key(3), Key(4), Key(5), Key(6), Key(7),
-      Key(8), Key(9), Key(10);
+  const Ordering ordering{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
 
   // Solve using that ordering:
   auto actualMPE = csp.optimize(ordering);

gtsam_unstable/partition/tests/testNestedDissection.cpp

@@ -34,7 +34,7 @@ TEST ( NestedDissection, oneIsland )
   fg.addBearingRange(2, 1, Rot2(), 0., bearingRangeNoise);
   fg.addPoseConstraint(1, Pose2());
 
-  Ordering ordering; ordering += x1, x2, l1;
+  const Ordering ordering{x1, x2, l1};
 
   int numNodeStopPartition = 1e3;
   int minNodesPerMap = 1e3;
@@ -61,7 +61,7 @@ TEST ( NestedDissection, TwoIslands )
   fg.addOdometry(3, 5, Pose2(), odoNoise);
   fg.addPoseConstraint(1, Pose2());
   fg.addPoseConstraint(4, Pose2());
-  Ordering ordering; ordering += x1, x2, x3, x4, x5;
+  const Ordering ordering{x1, x2, x3, x4, x5};
 
   int numNodeStopPartition = 2;
   int minNodesPerMap = 1;
@@ -96,7 +96,7 @@ TEST ( NestedDissection, FourIslands )
   fg.addOdometry(3, 5, Pose2(), odoNoise);
   fg.addPoseConstraint(1, Pose2());
   fg.addPoseConstraint(4, Pose2());
-  Ordering ordering; ordering += x1, x2, x3, x4, x5;
+  const Ordering ordering{x1, x2, x3, x4, x5};
 
   int numNodeStopPartition = 2;
   int minNodesPerMap = 1;
@@ -144,7 +144,7 @@ TEST ( NestedDissection, weekLinks )
   fg.addPoseConstraint(1, Pose2());
   fg.addPoseConstraint(4, Pose2());
   fg.addPoseConstraint(5, Pose2());
-  Ordering ordering; ordering += x1, x2, x3, x4, x5, l6;
+  const Ordering ordering{x1, x2, x3, x4, x5, l6};
 
   int numNodeStopPartition = 2;
   int minNodesPerMap = 1;
@@ -206,7 +206,7 @@ TEST ( NestedDissection, manual_cuts )
   fg.addPrior(x0, Pose2(0.1, 0, 0), priorNoise);
 
   // generate ordering
-  Ordering ordering; ordering += x0, x1, x2, l1, l2, l3, l4, l5, l6;
+  const Ordering ordering{x0, x1, x2, l1, l2, l3, l4, l5, l6};
 
   // define cuts
   std::shared_ptr<Cuts> cuts(new Cuts());
@@ -301,9 +301,9 @@ TEST( NestedDissection, Graph3D) {
     graph.addMeasurement(3, j, cameras[3].project(points[j-1]).expmap(measurementZeroNoise->sample()), measurementNoise);
 
   // make an easy ordering
-  Ordering ordering; ordering += x0, x1, x2, x3;
+  const Ordering ordering{x0, x1, x2, x3};
   for (int j=1; j<=24; j++)
-    ordering += Symbol('l', j);
+    ordering.push_back(Symbol('l', j));
 
   // nested dissection
   const int numNodeStopPartition = 10;

tests/testGaussianBayesTreeB.cpp

@@ -114,8 +114,7 @@ TEST(GaussianBayesTree, balanced_smoother_marginals) {
   GaussianFactorGraph smoother = createSmoother(7);
 
   // Create the Bayes tree
-  Ordering ordering;
-  ordering += X(1), X(3), X(5), X(7), X(2), X(6), X(4);
+  const Ordering ordering{X(1), X(3), X(5), X(7), X(2), X(6), X(4)};
   GaussianBayesTree bayesTree = *smoother.eliminateMultifrontal(ordering);
 
   VectorValues actualSolution = bayesTree.optimize();
@@ -162,8 +161,7 @@ TEST( GaussianBayesTree, balanced_smoother_shortcuts )
   GaussianFactorGraph smoother = createSmoother(7);
 
   // Create the Bayes tree
-  Ordering ordering;
-  ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
+  const Ordering ordering{X(1), X(3), X(5), X(7), X(2), X(6), X(4)};
   GaussianBayesTree bayesTree = *smoother.eliminateMultifrontal(ordering);
 
   // Check the conditional P(Root|Root)
@@ -194,8 +192,7 @@ TEST( GaussianBayesTree, balanced_smoother_shortcuts )
 //TEST( BayesTree, balanced_smoother_clique_marginals )
 //{
 //  // Create smoother with 7 nodes
-//  Ordering ordering;
-//  ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
+//  const Ordering ordering{X(1),X(3),X(5),X(7),X(2),X(6),X(4)};
 //  GaussianFactorGraph smoother = createSmoother(7, ordering).first;
 //
 //  // Create the Bayes tree
@@ -223,8 +220,7 @@ TEST( GaussianBayesTree, balanced_smoother_shortcuts )
 TEST( GaussianBayesTree, balanced_smoother_joint )
 {
   // Create smoother with 7 nodes
-  Ordering ordering;
-  ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
+  const Ordering ordering{X(1), X(3), X(5), X(7), X(2), X(6), X(4)};
   GaussianFactorGraph smoother = createSmoother(7);
 
   // Create the Bayes tree, expected to look like:

tests/testGaussianFactorGraphB.cpp

@@ -78,8 +78,7 @@ TEST(GaussianFactorGraph, eliminateOne_x1) {
 
 /* ************************************************************************* */
 TEST(GaussianFactorGraph, eliminateOne_x2) {
-  Ordering ordering;
-  ordering += X(2), L(1), X(1);
+  const Ordering ordering{X(2), L(1), X(1)};
   GaussianFactorGraph fg = createGaussianFactorGraph();
   auto actual = EliminateQR(fg, Ordering{X(2)}).first;
 
@@ -94,8 +93,7 @@ TEST(GaussianFactorGraph, eliminateOne_x2) {
 
 /* ************************************************************************* */
 TEST(GaussianFactorGraph, eliminateOne_l1) {
-  Ordering ordering;
-  ordering += L(1), X(1), X(2);
+  const Ordering ordering{L(1), X(1), X(2)};
   GaussianFactorGraph fg = createGaussianFactorGraph();
   auto actual = EliminateQR(fg, Ordering{L(1)}).first;
 
@@ -282,8 +280,7 @@ TEST(GaussianFactorGraph, elimination) {
   fg.emplace_shared<JacobianFactor>(X(2), Ap, b, sigma);
 
   // Eliminate
-  Ordering ordering;
-  ordering += X(1), X(2);
+  const Ordering ordering{X(1), X(2)};
   GaussianBayesNet bayesNet = *fg.eliminateSequential();
 
   // Check matrix
@@ -348,7 +345,6 @@ static SharedDiagonal model = noiseModel::Isotropic::Sigma(2,1);
 /* ************************************************************************* */
 TEST(GaussianFactorGraph, replace)
 {
-  Ordering ord; ord += X(1),X(2),X(3),X(4),X(5),X(6);
   SharedDiagonal noise(noiseModel::Isotropic::Sigma(3, 1.0));
 
   GaussianFactorGraph::sharedFactor f1(new JacobianFactor(

tests/testGaussianISAM.cpp

@@ -33,7 +33,7 @@ using symbol_shorthand::L;
 TEST( ISAM, iSAM_smoother )
 {
   Ordering ordering;
-  for (int t = 1; t <= 7; t++) ordering += X(t);
+  for (int t = 1; t <= 7; t++) ordering.push_back(X(t));
 
   // Create smoother with 7 nodes
   GaussianFactorGraph smoother = createSmoother(7);

tests/testGaussianJunctionTreeB.cpp

@@ -78,20 +78,7 @@ TEST( GaussianJunctionTreeB, constructor2 ) {
   SymbolicEliminationTree stree(*symbolic, ordering);
   GaussianJunctionTree actual(etree);
 
-  Ordering o324;
-  o324 += X(3), X(2), X(4);
-  Ordering o56;
-  o56 += X(5), X(6);
-  Ordering o7;
-  o7 += X(7);
-  Ordering o1;
-  o1 += X(1);
-
-  // Can no longer test these:
-//  Ordering sep1;
-//  Ordering sep2; sep2 += X(4);
-//  Ordering sep3; sep3 += X(6);
-//  Ordering sep4; sep4 += X(2);
+  const Ordering o324{X(3), X(2), X(4)}, o56{X(5), X(6)}, o7{X(7)}, o1{X(1)};
 
   GaussianJunctionTree::sharedNode x324 = actual.roots().front();
   LONGS_EQUAL(2, x324->children.size());
@@ -228,8 +215,7 @@ TEST(GaussianJunctionTreeB, optimizeMultiFrontal2) {
 //  init.insert(X(0), Pose2());
 //  init.insert(X(1), Pose2(1.0, 0.0, 0.0));
 //
-//  Ordering ordering;
-//  ordering += X(1), X(0);
+//  const Ordering ordering{X(1), X(0)};
 //
 //  GaussianFactorGraph gfg = *fg.linearize(init, ordering);
 //

tests/testNonlinearFactorGraph.cpp

@@ -78,13 +78,13 @@ TEST( NonlinearFactorGraph, keys )
 /* ************************************************************************* */
 TEST( NonlinearFactorGraph, GET_ORDERING)
 {
-  Ordering expected; expected += L(1), X(2), X(1); // For starting with l1,x1,x2
+  const Ordering expected{L(1), X(2), X(1)};  // For starting with l1,x1,x2
   NonlinearFactorGraph nlfg = createNonlinearFactorGraph();
   Ordering actual = Ordering::Colamd(nlfg);
   EXPECT(assert_equal(expected,actual));
 
   // Constrained ordering - put x2 at the end
-  Ordering expectedConstrained; expectedConstrained += L(1), X(1), X(2);
+  const Ordering expectedConstrained{L(1), X(1), X(2)};
   FastMap<Key, int> constraints;
   constraints[X(2)] = 1;
   Ordering actualConstrained = Ordering::ColamdConstrained(nlfg, constraints);
@@ -198,8 +198,7 @@ TEST(NonlinearFactorGraph, UpdateCholesky) {
   EXPECT(assert_equal(expected, fg.updateCholesky(initial)));
 
   // solve with Ordering
-  Ordering ordering;
-  ordering += L(1), X(2), X(1);
+  const Ordering ordering{L(1), X(2), X(1)};
   EXPECT(assert_equal(expected, fg.updateCholesky(initial, ordering)));
 
   // solve with new method, heavily damped
@@ -251,8 +250,7 @@ TEST(testNonlinearFactorGraph, eliminate) {
   auto linearized = graph.linearize(values);
 
   // Eliminate
-  Ordering ordering;
-  ordering += 11, 21, 12, 22;
+  const Ordering ordering{11, 21, 12, 22};
   auto bn = linearized->eliminateSequential(ordering);
   EXPECT_LONGS_EQUAL(4, bn->size());
 }

tests/testSerializationSlam.cpp

@@ -217,7 +217,7 @@ BOOST_CLASS_EXPORT_GUID(GenericStereoFactor3D, "gtsam::GenericStereoFactor3D")
 TEST (testSerializationSLAM, smallExample_linear) {
   using namespace example;
 
-  Ordering ordering; ordering += X(1),X(2),L(1);
+  const Ordering ordering{X(1), X(2), L(1)};
   EXPECT(equalsObj(ordering));
   EXPECT(equalsXML(ordering));
   EXPECT(equalsBinary(ordering));

tests/testSubgraphPreconditioner.cpp

@@ -42,11 +42,9 @@ Symbol key(int x, int y) { return symbol_shorthand::X(1000 * x + y); }
 /* ************************************************************************* */
 TEST(SubgraphPreconditioner, planarOrdering) {
   // Check canonical ordering
-  Ordering expected, ordering = planarOrdering(3);
-  expected +=
-      key(3, 3), key(2, 3), key(1, 3),
-      key(3, 2), key(2, 2), key(1, 2),
-      key(3, 1), key(2, 1), key(1, 1);
+  Ordering ordering = planarOrdering(3),
+           expected{key(3, 3), key(2, 3), key(1, 3), key(3, 2), key(2, 2),
+                    key(1, 2), key(3, 1), key(2, 1), key(1, 1)};
   EXPECT(assert_equal(expected, ordering));
 }
 

timing/timeGaussianFactor.cpp

@@ -115,8 +115,7 @@ int main()
   cout << ((double)n/seconds) << " calls/second" << endl;
 
   // time matrix_augmented
-//  Ordering ordering;
-//  ordering += _x2_, _l1_, _x1_;
+//  const Ordering ordering{_x2_, _l1_, _x1_};
 //  size_t n1 = 10000000;
 //  timeLog = clock();
 //