Merge branch 'develop' into feaure/remove_misc_boost

examples/ISAM2Example_SmartFactor.cpp

@@ -87,9 +87,8 @@ int main(int argc, char* argv[]) {
     result.print();
 
     cout << "Detailed results:" << endl;
-    for (auto keyedStatus : result.detail->variableStatus) {
+    for (auto& [key, status] : result.detail->variableStatus) {
-      const auto& status = keyedStatus.second;
+      PrintKey(key);
-      PrintKey(keyedStatus.first);
       cout << " {" << endl;
       cout << "reeliminated: " << status.isReeliminated << endl;
       cout << "relinearized above thresh: " << status.isAboveRelinThreshold

examples/Pose3Localization.cpp

@@ -74,8 +74,8 @@ int main(const int argc, const char* argv[]) {
 
   // Calculate and print marginal covariances for all variables
   Marginals marginals(*graph, result);
-  for (const auto& key_pose : result.extract<Pose3>()) {
+  for (const auto& [key, pose] : result.extract<Pose3>()) {
-    std::cout << marginals.marginalCovariance(key_pose.first) << endl;
+    std::cout << marginals.marginalCovariance(key) << endl;
   }
   return 0;
 }

examples/SFMExampleExpressions_bal.cpp

@@ -77,9 +77,7 @@ int main(int argc, char* argv[]) {
   for (const SfmTrack& track : mydata.tracks) {
     // Leaf expression for j^th point
     Point3_ point_('p', j);
-    for (const SfmMeasurement& m : track.measurements) {
+    for (const auto& [i, uv] : track.measurements) {
-      size_t i = m.first;
-      Point2 uv = m.second;
       // Leaf expression for i^th camera
       Expression<SfmCamera> camera_(C(i));
       // Below an expression for the prediction of the measurement:

examples/SFMExample_bal.cpp

@@ -56,9 +56,7 @@ int main (int argc, char* argv[]) {
   // Add measurements to the factor graph
   size_t j = 0;
   for(const SfmTrack& track: mydata.tracks) {
-    for(const SfmMeasurement& m: track.measurements) {
+    for (const auto& [i, uv] : track.measurements) {
-      size_t i = m.first;
-      Point2 uv = m.second;
       graph.emplace_shared<MyFactor>(uv, noise, C(i), P(j)); // note use of shorthand symbols C and P
     }
     j += 1;

examples/SFMExample_bal_COLAMD_METIS.cpp

@@ -57,9 +57,7 @@ int main(int argc, char* argv[]) {
   // Add measurements to the factor graph
   size_t j = 0;
   for (const SfmTrack& track : mydata.tracks) {
-    for (const SfmMeasurement& m : track.measurements) {
+    for (const auto& [i, uv] : track.measurements) {
-      size_t i = m.first;
-      Point2 uv = m.second;
       graph.emplace_shared<MyFactor>(
           uv, noise, C(i), P(j));  // note use of shorthand symbols C and P
     }

examples/SolverComparer.cpp

@@ -557,12 +557,12 @@ void runPerturb()
 
   // Perturb values
   VectorValues noise;
-  for(const auto& key_dim: initial.dims())
+  for(const auto& [key, dim]: initial.dims())
   {
-    Vector noisev(key_dim.second);
+    Vector noisev(dim);
     for(Vector::Index i = 0; i < noisev.size(); ++i)
       noisev(i) = normal(rng);
-    noise.insert(key_dim.first, noisev);
+    noise.insert(key, noisev);
   }
   Values perturbed = initial.retract(noise);
 

gtsam/base/DSFMap.h

@@ -50,7 +50,7 @@ class DSFMap {
     iterator it = entries_.find(key);
     // if key does not exist, create and return itself
     if (it == entries_.end()) {
-      it = entries_.insert(std::make_pair(key, empty)).first;
+      it = entries_.insert({key, empty}).first;
       it->second.parent_ = it;
       it->second.rank_ = 0;
     }

gtsam/base/FastMap.h

@@ -63,7 +63,7 @@ public:
   }
 
   /** Handy 'insert' function for Matlab wrapper */
-  bool insert2(const KEY& key, const VALUE& val) { return Base::insert(std::make_pair(key, val)).second; }
+  bool insert2(const KEY& key, const VALUE& val) { return Base::insert({key, val}).second; }
 
   /** Handy 'exists' function */
   bool exists(const KEY& e) const { return this->find(e) != this->end(); }

gtsam/base/Matrix.cpp

@@ -248,8 +248,7 @@ pair<Matrix,Matrix> qr(const Matrix& A) {
       xjm(k) = R(j+k, j);
 
     // calculate the Householder vector v
-    double beta; Vector vjm;
+    const auto [beta, vjm] = house(xjm);
-    std::tie(beta,vjm) = house(xjm);
 
     // pad with zeros to get m-dimensional vector v
     for(size_t k = 0 ; k < m; k++)

gtsam/base/WeightedSampler.h

@@ -69,7 +69,7 @@ class WeightedSampler {
     static const double kexp1 = std::exp(1.0);
     for (auto it = weights.begin(); it != weights.begin() + numSamples; ++it) {
       const double k_i = kexp1 / *it;
-      reservoir.push(std::make_pair(k_i, it - weights.begin() + 1));
+      reservoir.push({k_i, it - weights.begin() + 1});
     }
 
     // Step 4: Repeat Steps 5–10 until the population is exhausted
@@ -110,7 +110,7 @@ class WeightedSampler {
         // Step 8: The item in reservoir with the minimum key is replaced by
         // item v_i
         reservoir.pop();
-        reservoir.push(std::make_pair(k_i, it - weights.begin() + 1));
+        reservoir.push({k_i, it - weights.begin() + 1});
       }
     }
 

gtsam/base/tests/testMatrix.cpp

@@ -857,8 +857,7 @@ TEST(Matrix, qr )
   Matrix expectedR = (Matrix(6, 4) << 15, 0, -8.3333, 0, 00, 11.1803, 0, -2.2361, 00, 0,
       7.4536, 0, 00, 0, 0, 10.9545, 00, 0, 0, 0, 00, 0, 0, 0).finished();
 
-  Matrix Q, R;
+  const auto [Q, R] = qr(A);
-  std::tie(Q, R) = qr(A);
   EXPECT(assert_equal(expectedQ, Q, 1e-4));
   EXPECT(assert_equal(expectedR, R, 1e-4));
   EXPECT(assert_equal(A, Q*R, 1e-14));
@@ -915,10 +914,7 @@ TEST(Matrix, weighted_elimination )
 
   // unpack and verify
   size_t i = 0;
-  for (const auto& tuple : solution) {
+  for (const auto& [r, di, sigma] : solution) {
-    Vector r;
-    double di, sigma;
-    std::tie(r, di, sigma) = tuple;
     EXPECT(assert_equal(r, expectedR.row(i))); // verify r
     DOUBLES_EQUAL(d(i), di, 1e-8); // verify d
     DOUBLES_EQUAL(newSigmas(i), sigma, 1e-5); // verify sigma
@@ -1142,10 +1138,7 @@ TEST(Matrix, DLT )
       1.89,         2.24,         3.99,         3.24,         3.84,         6.84,        18.09,        21.44,        38.19,
       2.24,         2.48,         6.24,         3.08,         3.41,         8.58,        24.64,        27.28,        68.64
   ).finished();
-  int rank;
+  const auto [rank,error,actual] = DLT(A);
-  double error;
-  Vector actual;
-  std::tie(rank,error,actual) = DLT(A);
   Vector expected = (Vector(9) << -0.0, 0.2357, 0.4714, -0.2357, 0.0, - 0.4714,-0.4714, 0.4714, 0.0).finished();
   EXPECT_LONGS_EQUAL(8,rank);
   EXPECT_DOUBLES_EQUAL(0,error,1e-8);

gtsam/base/tests/testVector.cpp

@@ -154,8 +154,7 @@ TEST(Vector, weightedPseudoinverse )
   Vector weights = sigmas.array().square().inverse();
 
   // perform solve
-  Vector actual; double precision;
+  const auto [actual, precision] = weightedPseudoinverse(x, weights);
-  std::tie(actual, precision) = weightedPseudoinverse(x, weights);
 
   // construct expected
   Vector expected(2);
@@ -179,8 +178,7 @@ TEST(Vector, weightedPseudoinverse_constraint )
   sigmas(0) = 0.0; sigmas(1) = 0.2;
   Vector weights = sigmas.array().square().inverse();
   // perform solve
-  Vector actual; double precision;
+  const auto [actual, precision] = weightedPseudoinverse(x, weights);
-  std::tie(actual, precision) = weightedPseudoinverse(x, weights);
 
   // construct expected
   Vector expected(2);
@@ -197,8 +195,7 @@ TEST(Vector, weightedPseudoinverse_nan )
   Vector a = (Vector(4) << 1., 0., 0., 0.).finished();
   Vector sigmas = (Vector(4) << 0.1, 0.1, 0., 0.).finished();
   Vector weights = sigmas.array().square().inverse();
-  Vector pseudo; double precision;
+  const auto [pseudo, precision] = weightedPseudoinverse(a, weights);
-  std::tie(pseudo, precision) = weightedPseudoinverse(a, weights);
 
   Vector expected = (Vector(4) << 1., 0., 0.,0.).finished();
   EXPECT(assert_equal(expected, pseudo));

gtsam/base/timing.cpp

@@ -219,10 +219,9 @@ size_t getTicTocID(const char *descriptionC) {
   static gtsam::FastMap<std::string, size_t> idMap;
 
   // Retrieve or add this string
-  gtsam::FastMap<std::string, size_t>::const_iterator it = idMap.find(
+  auto it = idMap.find(description);
-      description);
   if (it == idMap.end()) {
-    it = idMap.insert(std::make_pair(description, nextId)).first;
+    it = idMap.insert({description, nextId}).first;
     ++nextId;
   }
 

gtsam/discrete/DecisionTree.h

@@ -424,10 +424,10 @@ namespace gtsam {
   template <typename L, typename T1, typename T2>
   std::pair<DecisionTree<L, T1>, DecisionTree<L, T2> > unzip(
       const DecisionTree<L, std::pair<T1, T2> >& input) {
-    return std::make_pair(
+    return {
         DecisionTree<L, T1>(input, [](std::pair<T1, T2> i) { return i.first; }),
-        DecisionTree<L, T2>(input,
+        DecisionTree<L, T2>(input, [](std::pair<T1, T2> i) { return i.second; })
-                            [](std::pair<T1, T2> i) { return i.second; }));
+    };
   }
 
 }  // namespace gtsam

gtsam/discrete/DiscreteFactorGraph.cpp

@@ -140,8 +140,7 @@ namespace gtsam {
                                                           orderedKeys, product);
     gttoc(lookup);
 
-    return std::make_pair(
+    return {std::dynamic_pointer_cast<DiscreteConditional>(lookup), max};
-        std::dynamic_pointer_cast<DiscreteConditional>(lookup), max);
   }
 
   /* ************************************************************************ */
@@ -223,7 +222,7 @@ namespace gtsam {
         std::make_shared<DiscreteConditional>(product, *sum, orderedKeys);
     gttoc(divide);
 
-    return std::make_pair(conditional, sum);
+    return {conditional, sum};
   }
 
   /* ************************************************************************ */

gtsam/discrete/tests/testDecisionTree.cpp

@@ -462,9 +462,7 @@ TEST(DecisionTree, unzip) {
   DTP tree(B, DTP(A, {0, "zero"}, {1, "one"}),
            DTP(A, {2, "two"}, {1337, "l33t"}));
 
-  DT1 dt1;
+  const auto [dt1, dt2] = unzip(tree);
-  DT2 dt2;
-  std::tie(dt1, dt2) = unzip(tree);
 
   DT1 tree1(B, DT1(A, 0, 1), DT1(A, 2, 1337));
   DT2 tree2(B, DT2(A, "zero", "one"), DT2(A, "two", "l33t"));

gtsam/discrete/tests/testDiscreteFactorGraph.cpp

@@ -109,9 +109,7 @@ TEST(DiscreteFactorGraph, test) {
   // Test EliminateDiscrete
   Ordering frontalKeys;
   frontalKeys += Key(0);
-  DiscreteConditional::shared_ptr conditional;
+  const auto [conditional, newFactor] = EliminateDiscrete(graph, frontalKeys);
-  DecisionTreeFactor::shared_ptr newFactor;
-  std::tie(conditional, newFactor) = EliminateDiscrete(graph, frontalKeys);
 
   // Check Conditional
   CHECK(conditional);
@@ -128,9 +126,7 @@ TEST(DiscreteFactorGraph, test) {
   Ordering ordering;
   ordering += Key(0), Key(1), Key(2);
   DiscreteEliminationTree etree(graph, ordering);
-  DiscreteBayesNet::shared_ptr actual;
+  const auto [actual, remainingGraph] = etree.eliminate(&EliminateDiscrete);
-  DiscreteFactorGraph::shared_ptr remainingGraph;
-  std::tie(actual, remainingGraph) = etree.eliminate(&EliminateDiscrete);
 
   // Check Bayes net
   DiscreteBayesNet expectedBayesNet;

gtsam/geometry/CalibratedCamera.h

@@ -223,7 +223,7 @@ public:
    * @return a pair of [start, end] indices into the tangent space vector
    */
   inline static std::pair<size_t, size_t> translationInterval() {
-    return std::make_pair(3, 5);
+    return {3, 5};
   }
 
   /// @}

gtsam/geometry/CameraSet.h

@@ -396,7 +396,7 @@ class CameraSet : public std::vector<CAMERA, Eigen::aligned_allocator<CAMERA>> {
 
     FastMap<Key, size_t> KeySlotMap;
     for (size_t slot = 0; slot < allKeys.size(); slot++)
-      KeySlotMap.insert(std::make_pair(allKeys[slot], slot));
+      KeySlotMap.emplace(allKeys[slot], slot);
 
     // Schur complement trick
     // G = F' * F - F' * E * P * E' * F

gtsam/geometry/Pose2.h

@@ -309,14 +309,14 @@ public:
    * exponential map parameterization
    * @return a pair of [start, end] indices into the tangent space vector
    */
-  inline static std::pair<size_t, size_t> translationInterval() { return std::make_pair(0, 1); }
+  inline static std::pair<size_t, size_t> translationInterval() { return {0, 1}; }
 
   /**
    * Return the start and end indices (inclusive) of the rotation component of the
    * exponential map parameterization
    * @return a pair of [start, end] indices into the tangent space vector
    */
-  static std::pair<size_t, size_t> rotationInterval() { return std::make_pair(2, 2); }
+  static std::pair<size_t, size_t> rotationInterval() { return {2, 2}; }
 
   /// Output stream operator
   GTSAM_EXPORT

gtsam/geometry/Pose3.h

@@ -364,7 +364,7 @@ public:
    * @return a pair of [start, end] indices into the tangent space vector
    */
   inline static std::pair<size_t, size_t> translationInterval() {
-    return std::make_pair(3, 5);
+    return {3, 5};
   }
 
   /**
@@ -373,7 +373,7 @@ public:
    * @return a pair of [start, end] indices into the tangent space vector
    */
   static std::pair<size_t, size_t> rotationInterval() {
-    return std::make_pair(0, 2);
+    return {0, 2};
   }
 
     /**

gtsam/geometry/Similarity2.cpp

@@ -31,9 +31,9 @@ namespace internal {
 static Point2Pairs SubtractCentroids(const Point2Pairs& abPointPairs,
                                      const Point2Pair& centroids) {
   Point2Pairs d_abPointPairs;
-  for (const Point2Pair& abPair : abPointPairs) {
+  for (const auto& [a, b] : abPointPairs) {
-    Point2 da = abPair.first - centroids.first;
+    Point2 da = a - centroids.first;
-    Point2 db = abPair.second - centroids.second;
+    Point2 db = b - centroids.second;
     d_abPointPairs.emplace_back(da, db);
   }
   return d_abPointPairs;
@@ -43,10 +43,8 @@ static Point2Pairs SubtractCentroids(const Point2Pairs& abPointPairs,
 static double CalculateScale(const Point2Pairs& d_abPointPairs,
                              const Rot2& aRb) {
   double x = 0, y = 0;
-  Point2 da, db;
 
-  for (const Point2Pair& d_abPair : d_abPointPairs) {
+  for (const auto& [da, db] : d_abPointPairs) {
-    std::tie(da, db) = d_abPair;
     const Vector2 da_prime = aRb * db;
     y += da.transpose() * da_prime;
     x += da_prime.transpose() * da_prime;
@@ -58,8 +56,8 @@ static double CalculateScale(const Point2Pairs& d_abPointPairs,
 /// Form outer product H.
 static Matrix2 CalculateH(const Point2Pairs& d_abPointPairs) {
   Matrix2 H = Z_2x2;
-  for (const Point2Pair& d_abPair : d_abPointPairs) {
+  for (const auto& [da, db] : d_abPointPairs) {
-    H += d_abPair.first * d_abPair.second.transpose();
+    H += da * db.transpose();
   }
   return H;
 }
@@ -186,9 +184,7 @@ Similarity2 Similarity2::Align(const Pose2Pairs& abPosePairs) {
   abPointPairs.reserve(n);
   // Below denotes the pose of the i'th object/camera/etc
   // in frame "a" or frame "b".
-  Pose2 aTi, bTi;
+  for (const auto& [aTi, bTi] : abPosePairs) {
-  for (const Pose2Pair& abPair : abPosePairs) {
-    std::tie(aTi, bTi) = abPair;
     const Rot2 aRb = aTi.rotation().compose(bTi.rotation().inverse());
     rotations.emplace_back(aRb);
     abPointPairs.emplace_back(aTi.translation(), bTi.translation());

gtsam/geometry/Similarity3.cpp

@@ -31,9 +31,9 @@ namespace internal {
 static Point3Pairs subtractCentroids(const Point3Pairs &abPointPairs,
                                     const Point3Pair &centroids) {
   Point3Pairs d_abPointPairs;
-  for (const Point3Pair& abPair : abPointPairs) {
+  for (const auto& [a, b] : abPointPairs) {
-    Point3 da = abPair.first - centroids.first;
+    Point3 da = a - centroids.first;
-    Point3 db = abPair.second - centroids.second;
+    Point3 db = b - centroids.second;
     d_abPointPairs.emplace_back(da, db);
   }
   return d_abPointPairs;
@@ -46,8 +46,7 @@ static double calculateScale(const Point3Pairs &d_abPointPairs,
                              const Rot3 &aRb) {
   double x = 0, y = 0;
   Point3 da, db;
-  for (const Point3Pair& d_abPair : d_abPointPairs) {
+  for (const auto& [da, db] : d_abPointPairs) {
-    std::tie(da, db) = d_abPair;
     const Vector3 da_prime = aRb * db;
     y += da.transpose() * da_prime;
     x += da_prime.transpose() * da_prime;
@@ -59,8 +58,8 @@ static double calculateScale(const Point3Pairs &d_abPointPairs,
 /// Form outer product H.
 static Matrix3 calculateH(const Point3Pairs &d_abPointPairs) {
   Matrix3 H = Z_3x3;
-  for (const Point3Pair& d_abPair : d_abPointPairs) {
+  for (const auto& [da, db] : d_abPointPairs) {
-    H += d_abPair.first * d_abPair.second.transpose();
+    H += da * db.transpose();
   }
   return H;
 }
@@ -184,8 +183,7 @@ Similarity3 Similarity3::Align(const vector<Pose3Pair> &abPosePairs) {
   abPointPairs.reserve(n);
   // Below denotes the pose of the i'th object/camera/etc in frame "a" or frame "b"
   Pose3 aTi, bTi;
-  for (const Pose3Pair &abPair : abPosePairs) {
+  for (const auto &[aTi, bTi] : abPosePairs) {
-    std::tie(aTi, bTi) = abPair;
     const Rot3 aRb = aTi.rotation().compose(bTi.rotation().inverse());
     rotations.emplace_back(aRb);
     abPointPairs.emplace_back(aTi.translation(), bTi.translation());

gtsam/geometry/tests/testPoint3.cpp

@@ -176,7 +176,7 @@ TEST(Point3, mean) {
 
 TEST(Point3, mean_pair) {
   Point3 a_mean(2, 2, 2), b_mean(-1, 1, 0);
-  Point3Pair expected = std::make_pair(a_mean, b_mean);
+  Point3Pair expected = {a_mean, b_mean};
   Point3 a1(0, 0, 0), a2(1, 2, 3), a3(5, 4, 3);
   Point3 b1(-1, 0, 0), b2(-2, 4, 0), b3(0, -1, 0);
   std::vector<Point3Pair> point_pairs{{a1, b1}, {a2, b2}, {a3, b3}};

gtsam/geometry/tests/testRot3.cpp

@@ -126,10 +126,8 @@ TEST( Rot3, AxisAngle2)
   // constructor from a rotation matrix, as doubles in *row-major* order.
   Rot3 R1(-0.999957, 0.00922903, 0.00203116, 0.00926964, 0.999739, 0.0208927, -0.0018374, 0.0209105, -0.999781);
   
-  Unit3 actualAxis;
-  double actualAngle;
   // convert Rot3 to quaternion using GTSAM
-  std::tie(actualAxis, actualAngle) = R1.axisAngle();
+  const auto [actualAxis, actualAngle] = R1.axisAngle();
   
   double expectedAngle = 3.1396582;
   CHECK(assert_equal(expectedAngle, actualAngle, 1e-5));
@@ -506,11 +504,9 @@ TEST( Rot3, yaw_pitch_roll )
 TEST( Rot3, RQ)
 {
   // Try RQ on a pure rotation
-  Matrix actualK;
+  const auto [actualK, actual] = RQ(R.matrix());
-  Vector actual;
-  std::tie(actualK, actual) = RQ(R.matrix());
   Vector expected = Vector3(0.14715, 0.385821, 0.231671);
-  CHECK(assert_equal(I_3x3,actualK));
+  CHECK(assert_equal(I_3x3, (Matrix)actualK));
   CHECK(assert_equal(expected,actual,1e-6));
 
   // Try using xyz call, asserting that Rot3::RzRyRx(x,y,z).xyz()==[x;y;z]
@@ -529,9 +525,9 @@ TEST( Rot3, RQ)
   // Try RQ to recover calibration from 3*3 sub-block of projection matrix
   Matrix K = (Matrix(3, 3) << 500.0, 0.0, 320.0, 0.0, 500.0, 240.0, 0.0, 0.0, 1.0).finished();
   Matrix A = K * R.matrix();
-  std::tie(actualK, actual) = RQ(A);
+  const auto [actualK2, actual2] = RQ(A);
-  CHECK(assert_equal(K,actualK));
+  CHECK(assert_equal(K, actualK2));
-  CHECK(assert_equal(expected,actual,1e-6));
+  CHECK(assert_equal(expected, actual2, 1e-6));
 }
 
 /* ************************************************************************* */

gtsam/geometry/triangulation.cpp

@@ -48,10 +48,7 @@ Vector4 triangulateHomogeneousDLT(
     A.row(row) = p.x() * projection.row(2) - projection.row(0);
     A.row(row + 1) = p.y() * projection.row(2) - projection.row(1);
   }
-  int rank;
+  const auto [rank, error, v] = DLT(A, rank_tol);
-  double error;
-  Vector v;
-  std::tie(rank, error, v) = DLT(A, rank_tol);
 
   if (rank < 3) throw(TriangulationUnderconstrainedException());
 
@@ -79,10 +76,7 @@ Vector4 triangulateHomogeneousDLT(
     A.row(row) = p.x() * projection.row(2) - p.z() * projection.row(0);
     A.row(row + 1) = p.y() * projection.row(2) - p.z() * projection.row(1);
   }
-  int rank;
+  const auto [rank, error, v] = DLT(A, rank_tol);
-  double error;
-  Vector v;
-  std::tie(rank, error, v) = DLT(A, rank_tol);
 
   if (rank < 3) throw(TriangulationUnderconstrainedException());
 

gtsam/geometry/triangulation.h

@@ -137,7 +137,7 @@ std::pair<NonlinearFactorGraph, Values> triangulationGraph(
     graph.emplace_shared<TriangulationFactor<Camera> > //
         (camera_i, measurements[i], model, landmarkKey);
   }
-  return std::make_pair(graph, values);
+  return {graph, values};
 }
 
 /**
@@ -165,7 +165,7 @@ std::pair<NonlinearFactorGraph, Values> triangulationGraph(
     graph.emplace_shared<TriangulationFactor<CAMERA> > //
         (camera_i, measurements[i], model? model : unit, landmarkKey);
   }
-  return std::make_pair(graph, values);
+  return {graph, values};
 }
 
 /**
@@ -193,9 +193,7 @@ Point3 triangulateNonlinear(const std::vector<Pose3>& poses,
     const SharedNoiseModel& model = nullptr) {
 
   // Create a factor graph and initial values
-  Values values;
+  const auto [graph, values] = triangulationGraph<CALIBRATION> //
-  NonlinearFactorGraph graph;
-  std::tie(graph, values) = triangulationGraph<CALIBRATION> //
       (poses, sharedCal, measurements, Symbol('p', 0), initialEstimate, model);
 
   return optimize(graph, values, Symbol('p', 0));
@@ -215,9 +213,7 @@ Point3 triangulateNonlinear(
     const SharedNoiseModel& model = nullptr) {
 
   // Create a factor graph and initial values
-  Values values;
+  const auto [graph, values] = triangulationGraph<CAMERA> //
-  NonlinearFactorGraph graph;
-  std::tie(graph, values) = triangulationGraph<CAMERA> //
       (cameras, measurements, Symbol('p', 0), initialEstimate, model);
 
   return optimize(graph, values, Symbol('p', 0));

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -251,9 +251,7 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
 #endif
 
   // Separate out decision tree into conditionals and remaining factors.
-  GaussianMixture::Conditionals conditionals;
+  const auto [conditionals, newFactors] = unzip(eliminationResults);
-  GaussianMixtureFactor::Factors newFactors;
-  std::tie(conditionals, newFactors) = unzip(eliminationResults);
 
   // Create the GaussianMixture from the conditionals
   auto gaussianMixture = std::make_shared<GaussianMixture>(

gtsam/hybrid/HybridJunctionTree.cpp

@@ -100,9 +100,7 @@ struct HybridConstructorTraversalData {
 
     Ordering keyAsOrdering;
     keyAsOrdering.push_back(node->key);
-    SymbolicConditional::shared_ptr conditional;
+    const auto [conditional, separatorFactor] =
-    SymbolicFactor::shared_ptr separatorFactor;
-    std::tie(conditional, separatorFactor) =
         internal::EliminateSymbolic(symbolicFactors, keyAsOrdering);
 
     // Store symbolic elimination results in the parent

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -131,9 +131,7 @@ TEST(HybridBayesNet, Choose) {
 
   const Ordering ordering(s.linearizationPoint.keys());
 
-  HybridBayesNet::shared_ptr hybridBayesNet;
+  const auto [hybridBayesNet, remainingFactorGraph] =
-  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
-  std::tie(hybridBayesNet, remainingFactorGraph) =
       s.linearizedFactorGraph.eliminatePartialSequential(ordering);
 
   DiscreteValues assignment;
@@ -162,9 +160,7 @@ TEST(HybridBayesNet, OptimizeAssignment) {
 
   const Ordering ordering(s.linearizationPoint.keys());
 
-  HybridBayesNet::shared_ptr hybridBayesNet;
+  const auto [hybridBayesNet, remainingFactorGraph] =
-  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
-  std::tie(hybridBayesNet, remainingFactorGraph) =
       s.linearizedFactorGraph.eliminatePartialSequential(ordering);
 
   DiscreteValues assignment;

gtsam/hybrid/tests/testHybridBayesTree.cpp

@@ -99,9 +99,7 @@ TEST(HybridBayesTree, OptimizeAssignment) {
   Ordering ordering;
   for (size_t k = 0; k < s.K; k++) ordering += X(k);
 
-  HybridBayesNet::shared_ptr hybridBayesNet;
+  const auto [hybridBayesNet, remainingFactorGraph] =
-  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
-  std::tie(hybridBayesNet, remainingFactorGraph) =
       s.linearizedFactorGraph.eliminatePartialSequential(ordering);
 
   GaussianBayesNet gbn = hybridBayesNet->choose(assignment);
@@ -143,9 +141,7 @@ TEST(HybridBayesTree, Optimize) {
   Ordering ordering;
   for (size_t k = 0; k < s.K; k++) ordering += X(k);
 
-  HybridBayesNet::shared_ptr hybridBayesNet;
+  const auto [hybridBayesNet, remainingFactorGraph] =
-  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
-  std::tie(hybridBayesNet, remainingFactorGraph) =
       s.linearizedFactorGraph.eliminatePartialSequential(ordering);
 
   DiscreteFactorGraph dfg;

gtsam/hybrid/tests/testHybridEstimation.cpp

@@ -239,10 +239,8 @@ std::vector<size_t> getDiscreteSequence(size_t x) {
  */
 AlgebraicDecisionTree<Key> getProbPrimeTree(
     const HybridGaussianFactorGraph& graph) {
-  HybridBayesNet::shared_ptr bayesNet;
-  HybridGaussianFactorGraph::shared_ptr remainingGraph;
   Ordering continuous(graph.continuousKeySet());
-  std::tie(bayesNet, remainingGraph) =
+  const auto [bayesNet, remainingGraph] =
       graph.eliminatePartialSequential(continuous);
 
   auto last_conditional = bayesNet->at(bayesNet->size() - 1);
@@ -297,9 +295,7 @@ TEST(HybridEstimation, Probability) {
 
   // Continuous elimination
   Ordering continuous_ordering(graph.continuousKeySet());
-  HybridBayesNet::shared_ptr bayesNet;
+  auto [bayesNet, discreteGraph] =
-  HybridGaussianFactorGraph::shared_ptr discreteGraph;
-  std::tie(bayesNet, discreteGraph) =
       graph.eliminatePartialSequential(continuous_ordering);
 
   // Discrete elimination
@@ -347,9 +343,7 @@ TEST(HybridEstimation, ProbabilityMultifrontal) {
 
   // Eliminate continuous
   Ordering continuous_ordering(graph.continuousKeySet());
-  HybridBayesTree::shared_ptr bayesTree;
+  const auto [bayesTree, discreteGraph] =
-  HybridGaussianFactorGraph::shared_ptr discreteGraph;
-  std::tie(bayesTree, discreteGraph) =
       graph.eliminatePartialMultifrontal(continuous_ordering);
 
   // Get the last continuous conditional which will have all the discrete keys

gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp

@@ -270,9 +270,7 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalTwoClique) {
   auto ordering_full =
       Ordering::ColamdConstrainedLast(hfg, {M(0), M(1), M(2), M(3)});
 
-  HybridBayesTree::shared_ptr hbt;
+  const auto [hbt, remaining] = hfg.eliminatePartialMultifrontal(ordering_full);
-  HybridGaussianFactorGraph::shared_ptr remaining;
-  std::tie(hbt, remaining) = hfg.eliminatePartialMultifrontal(ordering_full);
 
   // 9 cliques in the bayes tree and 0 remaining variables to eliminate.
   EXPECT_LONGS_EQUAL(9, hbt->size());
@@ -327,10 +325,9 @@ TEST(HybridGaussianFactorGraph, Switching) {
     std::transform(naturalX.begin(), naturalX.end(), std::back_inserter(ordX),
                    [](int x) { return X(x); });
 
-    KeyVector ndX;
+    auto [ndX, lvls] = makeBinaryOrdering(ordX);
-    std::vector<int> lvls;
-    std::tie(ndX, lvls) = makeBinaryOrdering(ordX);
     std::copy(ndX.begin(), ndX.end(), std::back_inserter(ordering));
+    // TODO(dellaert): this has no effect!
     for (auto &l : lvls) {
       l = -l;
     }
@@ -341,11 +338,9 @@ TEST(HybridGaussianFactorGraph, Switching) {
     std::vector<Key> ordC;
     std::transform(naturalC.begin(), naturalC.end(), std::back_inserter(ordC),
                    [](int x) { return M(x); });
-    KeyVector ndC;
-    std::vector<int> lvls;
 
     // std::copy(ordC.begin(), ordC.end(), std::back_inserter(ordering));
-    std::tie(ndC, lvls) = makeBinaryOrdering(ordC);
+    const auto [ndC, lvls] = makeBinaryOrdering(ordC);
     std::copy(ndC.begin(), ndC.end(), std::back_inserter(ordering));
   }
   auto ordering_full = Ordering(ordering);
@@ -353,9 +348,7 @@ TEST(HybridGaussianFactorGraph, Switching) {
   // GTSAM_PRINT(*hfg);
   // GTSAM_PRINT(ordering_full);
 
-  HybridBayesTree::shared_ptr hbt;
+  const auto [hbt, remaining] = hfg->eliminatePartialMultifrontal(ordering_full);
-  HybridGaussianFactorGraph::shared_ptr remaining;
-  std::tie(hbt, remaining) = hfg->eliminatePartialMultifrontal(ordering_full);
 
   // 12 cliques in the bayes tree and 0 remaining variables to eliminate.
   EXPECT_LONGS_EQUAL(12, hbt->size());
@@ -388,10 +381,9 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
     std::transform(naturalX.begin(), naturalX.end(), std::back_inserter(ordX),
                    [](int x) { return X(x); });
 
-    KeyVector ndX;
+    auto [ndX, lvls] = makeBinaryOrdering(ordX);
-    std::vector<int> lvls;
-    std::tie(ndX, lvls) = makeBinaryOrdering(ordX);
     std::copy(ndX.begin(), ndX.end(), std::back_inserter(ordering));
+    // TODO(dellaert): this has no effect!
     for (auto &l : lvls) {
       l = -l;
     }
@@ -402,18 +394,14 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
     std::vector<Key> ordC;
     std::transform(naturalC.begin(), naturalC.end(), std::back_inserter(ordC),
                    [](int x) { return M(x); });
-    KeyVector ndC;
-    std::vector<int> lvls;
 
     // std::copy(ordC.begin(), ordC.end(), std::back_inserter(ordering));
-    std::tie(ndC, lvls) = makeBinaryOrdering(ordC);
+    const auto [ndC, lvls] = makeBinaryOrdering(ordC);
     std::copy(ndC.begin(), ndC.end(), std::back_inserter(ordering));
   }
   auto ordering_full = Ordering(ordering);
 
-  HybridBayesTree::shared_ptr hbt;
+  const auto [hbt, remaining] = hfg->eliminatePartialMultifrontal(ordering_full);
-  HybridGaussianFactorGraph::shared_ptr remaining;
-  std::tie(hbt, remaining) = hfg->eliminatePartialMultifrontal(ordering_full);
 
   auto new_fg = makeSwitchingChain(12);
   auto isam = HybridGaussianISAM(*hbt);
@@ -482,9 +470,7 @@ TEST(HybridGaussianFactorGraph, SwitchingTwoVar) {
     ordering_partial.emplace_back(X(i));
     ordering_partial.emplace_back(Y(i));
   }
-  HybridBayesNet::shared_ptr hbn;
+  const auto [hbn, remaining] = hfg->eliminatePartialSequential(ordering_partial);
-  HybridGaussianFactorGraph::shared_ptr remaining;
-  std::tie(hbn, remaining) = hfg->eliminatePartialSequential(ordering_partial);
 
   EXPECT_LONGS_EQUAL(14, hbn->size());
   EXPECT_LONGS_EQUAL(11, remaining->size());
@@ -847,10 +833,9 @@ TEST(HybridGaussianFactorGraph, EliminateSwitchingNetwork) {
   EXPECT(ratioTest(bn, measurements, fg));
 
   // Do elimination of X(2) only:
-  auto result = fg.eliminatePartialSequential(Ordering{X(2)});
+  auto [bn1, fg1] = fg.eliminatePartialSequential(Ordering{X(2)});
-  auto fg1 = *result.second;
+  fg1->push_back(*bn1);
-  fg1.push_back(*result.first);
+  EXPECT(ratioTest(bn, measurements, *fg1));
-  EXPECT(ratioTest(bn, measurements, fg1));
 
   // Create ordering that eliminates in time order, then discrete modes:
   Ordering ordering {X(2), X(1), X(0), N(0), N(1), N(2), M(1), M(2)};

gtsam/hybrid/tests/testHybridGaussianISAM.cpp

@@ -132,9 +132,7 @@ TEST(HybridGaussianElimination, IncrementalInference) {
   ordering += X(2);
 
   // Now we calculate the expected factors using full elimination
-  HybridBayesTree::shared_ptr expectedHybridBayesTree;
+  const auto [expectedHybridBayesTree, expectedRemainingGraph] =
-  HybridGaussianFactorGraph::shared_ptr expectedRemainingGraph;
-  std::tie(expectedHybridBayesTree, expectedRemainingGraph) =
       switching.linearizedFactorGraph.eliminatePartialMultifrontal(ordering);
 
   // The densities on X(0) should be the same
@@ -231,9 +229,7 @@ TEST(HybridGaussianElimination, Approx_inference) {
   }
 
   // Now we calculate the actual factors using full elimination
-  HybridBayesTree::shared_ptr unprunedHybridBayesTree;
+  const auto [unprunedHybridBayesTree, unprunedRemainingGraph] =
-  HybridGaussianFactorGraph::shared_ptr unprunedRemainingGraph;
-  std::tie(unprunedHybridBayesTree, unprunedRemainingGraph) =
       switching.linearizedFactorGraph.eliminatePartialMultifrontal(ordering);
 
   size_t maxNrLeaves = 5;

gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp

@@ -350,10 +350,7 @@ TEST(HybridGaussianElimination, EliminateHybrid_2_Variable) {
   ordering += X(0);
   ordering += X(1);
 
-  HybridConditional::shared_ptr hybridConditionalMixture;
+  const auto [hybridConditionalMixture, factorOnModes] =
-  std::shared_ptr<Factor> factorOnModes;
-
-  std::tie(hybridConditionalMixture, factorOnModes) =
       EliminateHybrid(factors, ordering);
 
   auto gaussianConditionalMixture =
@@ -385,9 +382,7 @@ TEST(HybridFactorGraph, Partial_Elimination) {
   for (size_t k = 0; k < self.K; k++) ordering += X(k);
 
   // Eliminate partially i.e. only continuous part.
-  HybridBayesNet::shared_ptr hybridBayesNet;
+  const auto [hybridBayesNet, remainingFactorGraph] =
-  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
-  std::tie(hybridBayesNet, remainingFactorGraph) =
       linearizedFactorGraph.eliminatePartialSequential(ordering);
 
   CHECK(hybridBayesNet);
@@ -415,8 +410,6 @@ TEST(HybridFactorGraph, Full_Elimination) {
   auto linearizedFactorGraph = self.linearizedFactorGraph;
 
   // We first do a partial elimination
-  HybridBayesNet::shared_ptr hybridBayesNet_partial;
-  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph_partial;
   DiscreteBayesNet discreteBayesNet;
 
   {
@@ -425,7 +418,7 @@ TEST(HybridFactorGraph, Full_Elimination) {
     for (size_t k = 0; k < self.K; k++) ordering += X(k);
 
     // Eliminate partially.
-    std::tie(hybridBayesNet_partial, remainingFactorGraph_partial) =
+    const auto [hybridBayesNet_partial, remainingFactorGraph_partial] =
         linearizedFactorGraph.eliminatePartialSequential(ordering);
 
     DiscreteFactorGraph discrete_fg;
@@ -489,9 +482,7 @@ TEST(HybridFactorGraph, Printing) {
   for (size_t k = 0; k < self.K; k++) ordering += X(k);
 
   // Eliminate partially.
-  HybridBayesNet::shared_ptr hybridBayesNet;
+  const auto [hybridBayesNet, remainingFactorGraph] =
-  HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
-  std::tie(hybridBayesNet, remainingFactorGraph) =
       linearizedFactorGraph.eliminatePartialSequential(ordering);
 
   string expected_hybridFactorGraph = R"(
@@ -721,11 +712,9 @@ TEST(HybridFactorGraph, DefaultDecisionTree) {
   ordering += X(1);
 
   HybridGaussianFactorGraph linearized = *fg.linearize(initialEstimate);
-  gtsam::HybridBayesNet::shared_ptr hybridBayesNet;
-  gtsam::HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
 
   // This should NOT fail
-  std::tie(hybridBayesNet, remainingFactorGraph) =
+  const auto [hybridBayesNet, remainingFactorGraph] =
       linearized.eliminatePartialSequential(ordering);
   EXPECT_LONGS_EQUAL(4, hybridBayesNet->size());
   EXPECT_LONGS_EQUAL(1, remainingFactorGraph->size());

gtsam/hybrid/tests/testHybridNonlinearISAM.cpp

@@ -149,9 +149,7 @@ TEST(HybridNonlinearISAM, IncrementalInference) {
   ordering += X(2);
 
   // Now we calculate the actual factors using full elimination
-  HybridBayesTree::shared_ptr expectedHybridBayesTree;
+  const auto [expectedHybridBayesTree, expectedRemainingGraph] =
-  HybridGaussianFactorGraph::shared_ptr expectedRemainingGraph;
-  std::tie(expectedHybridBayesTree, expectedRemainingGraph) =
       switching.linearizedFactorGraph
           .BaseEliminateable::eliminatePartialMultifrontal(ordering);
 
@@ -250,9 +248,7 @@ TEST(HybridNonlinearISAM, Approx_inference) {
   }
 
   // Now we calculate the actual factors using full elimination
-  HybridBayesTree::shared_ptr unprunedHybridBayesTree;
+  const auto [unprunedHybridBayesTree, unprunedRemainingGraph] =
-  HybridGaussianFactorGraph::shared_ptr unprunedRemainingGraph;
-  std::tie(unprunedHybridBayesTree, unprunedRemainingGraph) =
       switching.linearizedFactorGraph
           .BaseEliminateable::eliminatePartialMultifrontal(ordering);
 

gtsam/inference/BayesTree-inst.h

@@ -245,7 +245,7 @@ namespace gtsam {
   void BayesTree<CLIQUE>::fillNodesIndex(const sharedClique& subtree) {
     // Add each frontal variable of this root node
     for(const Key& j: subtree->conditional()->frontals()) {
-      bool inserted = nodes_.insert(std::make_pair(j, subtree)).second;
+      bool inserted = nodes_.insert({j, subtree}).second;
       assert(inserted); (void)inserted;
     }
     // Fill index for each child
@@ -360,9 +360,10 @@ namespace gtsam {
           C1_minus_B.assign(C1_minus_B_set.begin(), C1_minus_B_set.end());
         }
         // Factor into C1\B | B.
-        sharedFactorGraph temp_remaining;
+        p_C1_B =
-        std::tie(p_C1_B, temp_remaining) =
+            FactorGraphType(p_C1_Bred)
-          FactorGraphType(p_C1_Bred).eliminatePartialMultifrontal(Ordering(C1_minus_B), function);
+                .eliminatePartialMultifrontal(Ordering(C1_minus_B), function)
+                .first;
       }
       std::shared_ptr<typename EliminationTraitsType::BayesTreeType> p_C2_B; {
         KeyVector C2_minus_B; {
@@ -372,9 +373,10 @@ namespace gtsam {
           C2_minus_B.assign(C2_minus_B_set.begin(), C2_minus_B_set.end());
         }
         // Factor into C2\B | B.
-        sharedFactorGraph temp_remaining;
+        p_C2_B =
-        std::tie(p_C2_B, temp_remaining) =
+            FactorGraphType(p_C2_Bred)
-          FactorGraphType(p_C2_Bred).eliminatePartialMultifrontal(Ordering(C2_minus_B), function);
+                .eliminatePartialMultifrontal(Ordering(C2_minus_B), function)
+                .first;
       }
       gttoc(Full_root_factoring);
 

gtsam/inference/ClusterTree-inst.h

@@ -210,7 +210,7 @@ struct EliminationData {
       // putting orphan subtrees in the index - they'll already be in the index of the ISAM2
       // object they're added to.
       for (const Key& j: myData.bayesTreeNode->conditional()->frontals())
-        nodesIndex_.insert(std::make_pair(j, myData.bayesTreeNode));
+        nodesIndex_.emplace(j, myData.bayesTreeNode);
 
       // Store remaining factor in parent's gathered factors
       if (!eliminationResult.second->empty()) {
@@ -273,7 +273,7 @@ EliminatableClusterTree<BAYESTREE, GRAPH>::eliminate(const Eliminate& function)
   }
 
   // Return result
-  return std::make_pair(result, remaining);
+  return {result, remaining};
 }
 
 } // namespace gtsam

gtsam/inference/EliminateableFactorGraph-inst.h

@@ -72,9 +72,7 @@ namespace gtsam {
       gttic(eliminateSequential);
       // Do elimination
       EliminationTreeType etree(asDerived(), (*variableIndex).get(), ordering);
-      std::shared_ptr<BayesNetType> bayesNet;
+      const auto [bayesNet, factorGraph] = etree.eliminate(function);
-      std::shared_ptr<FactorGraphType> factorGraph;
-      std::tie(bayesNet,factorGraph) = etree.eliminate(function);
       // If any factors are remaining, the ordering was incomplete
       if(!factorGraph->empty())
         throw InconsistentEliminationRequested();
@@ -136,9 +134,7 @@ namespace gtsam {
       // Do elimination with given ordering
       EliminationTreeType etree(asDerived(), (*variableIndex).get(), ordering);
       JunctionTreeType junctionTree(etree);
-      std::shared_ptr<BayesTreeType> bayesTree;
+      const auto [bayesTree, factorGraph] = junctionTree.eliminate(function);
-      std::shared_ptr<FactorGraphType> factorGraph;
-      std::tie(bayesTree,factorGraph) = junctionTree.eliminate(function);
       // If any factors are remaining, the ordering was incomplete
       if(!factorGraph->empty())
         throw InconsistentEliminationRequested();
@@ -274,9 +270,7 @@ namespace gtsam {
       gttic(marginalMultifrontalBayesNet);
       // An ordering was provided for the marginalized variables, so we can first eliminate them
       // in the order requested.
-      std::shared_ptr<BayesTreeType> bayesTree;
+      const auto [bayesTree, factorGraph] =
-      std::shared_ptr<FactorGraphType> factorGraph;
-      std::tie(bayesTree,factorGraph) =
         eliminatePartialMultifrontal(marginalizedVariableOrdering, function, variableIndex);
 
       if(const Ordering* varsAsOrdering = boost::get<const Ordering&>(&variables))
@@ -341,9 +335,7 @@ namespace gtsam {
       gttic(marginalMultifrontalBayesTree);
       // An ordering was provided for the marginalized variables, so we can first eliminate them
       // in the order requested.
-      std::shared_ptr<BayesTreeType> bayesTree;
+      const auto [bayesTree, factorGraph] =
-      std::shared_ptr<FactorGraphType> factorGraph;
-      std::tie(bayesTree,factorGraph) =
         eliminatePartialMultifrontal(marginalizedVariableOrdering, function, variableIndex);
 
       if(const Ordering* varsAsOrdering = boost::get<const Ordering&>(&variables))

gtsam/inference/EliminationTree-inst.h

@@ -198,7 +198,7 @@ namespace gtsam {
     allRemainingFactors->push_back(remainingFactors.begin(), remainingFactors.end());
 
     // Return result
-    return std::make_pair(result, allRemainingFactors);
+    return {result, allRemainingFactors};
   }
 
   /* ************************************************************************* */
@@ -218,13 +218,13 @@ namespace gtsam {
     // Add roots in sorted order
     {
       FastMap<Key,sharedNode> keys;
-      for(const sharedNode& root: this->roots_) { keys.insert(std::make_pair(root->key, root)); }
+      for(const sharedNode& root: this->roots_) { keys.emplace(root->key, root); }
       typedef typename FastMap<Key,sharedNode>::value_type Key_Node;
       for(const Key_Node& key_node: keys) { stack1.push(key_node.second); }
     }
     {
       FastMap<Key,sharedNode> keys;
-      for(const sharedNode& root: expected.roots_) { keys.insert(std::make_pair(root->key, root)); }
+      for(const sharedNode& root: expected.roots_) { keys.emplace(root->key, root); }
       typedef typename FastMap<Key,sharedNode>::value_type Key_Node;
       for(const Key_Node& key_node: keys) { stack2.push(key_node.second); }
     }
@@ -258,13 +258,13 @@ namespace gtsam {
       // Add children in sorted order
       {
         FastMap<Key,sharedNode> keys;
-        for(const sharedNode& node: node1->children) { keys.insert(std::make_pair(node->key, node)); }
+        for(const sharedNode& node: node1->children) { keys.emplace(node->key, node); }
         typedef typename FastMap<Key,sharedNode>::value_type Key_Node;
         for(const Key_Node& key_node: keys) { stack1.push(key_node.second); }
       }
       {
         FastMap<Key,sharedNode> keys;
-        for(const sharedNode& node: node2->children) { keys.insert(std::make_pair(node->key, node)); }
+        for(const sharedNode& node: node2->children) { keys.emplace(node->key, node); }
         typedef typename FastMap<Key,sharedNode>::value_type Key_Node;
         for(const Key_Node& key_node: keys) { stack2.push(key_node.second); }
       }

gtsam/inference/JunctionTree-inst.h

@@ -83,10 +83,8 @@ struct ConstructorTraversalData {
 
     Ordering keyAsOrdering;
     keyAsOrdering.push_back(ETreeNode->key);
-    SymbolicConditional::shared_ptr myConditional;
+    const auto [myConditional, mySeparatorFactor] =
-    SymbolicFactor::shared_ptr mySeparatorFactor;
+        internal::EliminateSymbolic(symbolicFactors, keyAsOrdering);
-    std::tie(myConditional, mySeparatorFactor) = internal::EliminateSymbolic(
-        symbolicFactors, keyAsOrdering);
 
     // Store symbolic elimination results in the parent
     myData.parentData->childSymbolicConditionals.push_back(myConditional);

gtsam/inference/VariableSlots.h

@@ -109,8 +109,7 @@ VariableSlots::VariableSlots(const FG& factorGraph)
       // we're combining.  Initially we put the max integer value in
       // the array entry for each factor that will indicate the factor
       // does not involve the variable.
-      iterator thisVarSlots; bool inserted;
+      auto [thisVarSlots, inserted] = this->insert({involvedVariable, FastVector<size_t>()});
-        std::tie(thisVarSlots, inserted) = this->insert(std::make_pair(involvedVariable, FastVector<size_t>()));
       if(inserted)
         thisVarSlots->second.resize(factorGraph.nrFactors(), Empty);
       thisVarSlots->second[jointFactorPos] = factorVarSlot;

gtsam/inference/graph-inl.h

@@ -48,13 +48,8 @@ public:
 /* ************************************************************************* */
 template<class KEY>
 std::list<KEY> predecessorMap2Keys(const PredecessorMap<KEY>& p_map) {
-
   typedef typename SGraph<KEY>::Vertex SVertex;
-
+  const auto [g, root, key2vertex] = gtsam::predecessorMap2Graph<SGraph<KEY>, SVertex, KEY>(p_map);
-  SGraph<KEY> g;
-  SVertex root;
-  std::map<KEY, SVertex> key2vertex;
-  std::tie(g, root, key2vertex) = gtsam::predecessorMap2Graph<SGraph<KEY>, SVertex, KEY>(p_map);
 
   // breadth first visit on the graph
   std::list<KEY> keys;
@@ -121,18 +116,16 @@ predecessorMap2Graph(const PredecessorMap<KEY>& p_map) {
   std::map<KEY, V> key2vertex;
   V v1, v2, root;
   bool foundRoot = false;
-  for(auto child_parent: p_map) {
+  for(const auto& [child, parent]: p_map) {
-    KEY child, parent;
-    std::tie(child,parent) = child_parent;
     if (key2vertex.find(child) == key2vertex.end()) {
        v1 = add_vertex(child, g);
-       key2vertex.insert(std::make_pair(child, v1));
+       key2vertex.emplace(child, v1);
      } else
        v1 = key2vertex[child];
 
     if (key2vertex.find(parent) == key2vertex.end()) {
        v2 = add_vertex(parent, g);
-       key2vertex.insert(std::make_pair(parent, v2));
+       key2vertex.emplace(parent, v2);
      } else
        v2 = key2vertex[parent];
 
@@ -180,7 +173,6 @@ std::shared_ptr<Values> composePoses(const G& graph, const PredecessorMap<KEY>&
     boost::property<boost::vertex_name_t, KEY>,
     boost::property<boost::edge_weight_t, POSE> > PoseGraph;
   typedef typename boost::graph_traits<PoseGraph>::vertex_descriptor PoseVertex;
-  typedef typename boost::graph_traits<PoseGraph>::edge_descriptor PoseEdge;
 
   PoseGraph g;
   PoseVertex root;
@@ -189,8 +181,6 @@ std::shared_ptr<Values> composePoses(const G& graph, const PredecessorMap<KEY>&
       predecessorMap2Graph<PoseGraph, PoseVertex, KEY>(tree);
 
   // attach the relative poses to the edges
-  PoseEdge edge12, edge21;
-  bool found1, found2;
   for(typename G::sharedFactor nl_factor: graph) {
 
     if (nl_factor->keys().size() > 2)
@@ -207,8 +197,8 @@ std::shared_ptr<Values> composePoses(const G& graph, const PredecessorMap<KEY>&
     PoseVertex v2 = key2vertex.find(key2)->second;
 
     POSE l1Xl2 = factor->measured();
-    std::tie(edge12, found1) = boost::edge(v1, v2, g);
+    const auto [edge12, found1] = boost::edge(v1, v2, g);
-    std::tie(edge21, found2) = boost::edge(v2, v1, g);
+    const auto [edge21, found2] = boost::edge(v2, v1, g);
     if (found1 && found2) throw std::invalid_argument ("composePoses: invalid spanning tree");
     if (!found1 && !found2) continue;
     if (found1)

gtsam/inference/graph.h

@@ -59,7 +59,7 @@ namespace gtsam {
   public:
     /** convenience insert so we can pass ints for TypedSymbol keys */
     inline void insert(const KEY& key, const KEY& parent) {
-      std::map<KEY, KEY>::insert(std::make_pair(key, parent));
+      std::map<KEY, KEY>::emplace(key, parent);
     }
   };
 

gtsam/linear/JacobianFactor.cpp

@@ -94,9 +94,7 @@ JacobianFactor::JacobianFactor(const HessianFactor& factor)
   Ab_.full() = factor.info().selfadjointView();
 
   // Do Cholesky to get a Jacobian
-  size_t maxrank;
+  const auto [maxrank, success] = choleskyCareful(Ab_.matrix());
-  bool success;
-  std::tie(maxrank, success) = choleskyCareful(Ab_.matrix());
 
   // Check that Cholesky succeeded OR it managed to factor the full Hessian.
   // THe latter case occurs with non-positive definite matrices arising from QP.
@@ -213,9 +211,7 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph,
       graph);
 
   // Count dimensions
-  FastVector<DenseIndex> varDims;
+  const auto [varDims, m, n] = _countDims(jacobians, orderedSlots);
-  DenseIndex m, n;
-  std::tie(varDims, m, n) = _countDims(jacobians, orderedSlots);
 
   // Allocate matrix and copy keys in order
   gttic(allocate);

gtsam/linear/KalmanFilter.cpp

@@ -38,12 +38,12 @@ KalmanFilter::solve(const GaussianFactorGraph& factorGraph) const {
 
   // Eliminate the graph using the provided Eliminate function
   Ordering ordering(factorGraph.keys());
-  GaussianBayesNet::shared_ptr bayesNet = //
+  const auto bayesNet = //
       factorGraph.eliminateSequential(ordering, function_);
 
   // As this is a filter, all we need is the posterior P(x_t).
   // This is the last GaussianConditional in the resulting BayesNet
-  GaussianConditional::shared_ptr posterior = *(--bayesNet->end());
+  GaussianConditional::shared_ptr posterior = bayesNet->back();
   return std::make_shared<GaussianDensity>(*posterior);
 }
 

gtsam/linear/SubgraphBuilder.cpp

@@ -475,7 +475,7 @@ std::pair<GaussianFactorGraph, GaussianFactorGraph> splitFactorGraph(
     remaining.remove(e.index);
   }
 
-  return std::make_pair(subgraphFactors, remaining);
+  return {subgraphFactors, remaining};
 }
 
 /*****************************************************************************/

gtsam/linear/SubgraphSolver.cpp

@@ -34,8 +34,7 @@ namespace gtsam {
 SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &Ab,
     const Parameters &parameters, const Ordering& ordering) :
     parameters_(parameters) {
-  GaussianFactorGraph Ab1, Ab2;
+  const auto [Ab1, Ab2] = splitGraph(Ab);
-  std::tie(Ab1, Ab2) = splitGraph(Ab);
   if (parameters_.verbosity())
     cout << "Split A into (A1) " << Ab1.size() << " and (A2) " << Ab2.size()
          << " factors" << endl;

gtsam/linear/VectorValues.cpp

@@ -43,7 +43,7 @@ namespace gtsam {
 #ifdef TBB_GREATER_EQUAL_2020
       values_.emplace(key, x.segment(j, n));
 #else
-      values_.insert(std::make_pair(key, x.segment(j, n)));
+      values_.insert({key, x.segment(j, n)});
 #endif
       j += n;
     }
@@ -56,7 +56,7 @@ namespace gtsam {
 #ifdef TBB_GREATER_EQUAL_2020
       values_.emplace(v.key, x.segment(j, v.dimension));
 #else
-      values_.insert(std::make_pair(v.key, x.segment(j, v.dimension)));
+      values_.insert({v.key, x.segment(j, v.dimension)});
 #endif
       j += v.dimension;
     }
@@ -70,7 +70,7 @@ namespace gtsam {
 #ifdef TBB_GREATER_EQUAL_2020
       result.values_.emplace(key, Vector::Zero(value.size()));
 #else
-      result.values_.insert(std::make_pair(key, Vector::Zero(value.size())));
+      result.values_.insert({key, Vector::Zero(value.size())});
 #endif
     return result;
   }
@@ -267,7 +267,7 @@ namespace gtsam {
 #ifdef TBB_GREATER_EQUAL_2020
       result.values_.emplace(j1->first, j1->second + j2->second);
 #else
-      result.values_.insert(std::make_pair(j1->first, j1->second + j2->second));
+      result.values_.insert({j1->first, j1->second + j2->second});
 #endif
 
     return result;
@@ -329,7 +329,7 @@ namespace gtsam {
 #ifdef TBB_GREATER_EQUAL_2020
       result.values_.emplace(j1->first, j1->second - j2->second);
 #else
-      result.values_.insert(std::make_pair(j1->first, j1->second - j2->second));
+      result.values_.insert({j1->first, j1->second - j2->second});
 #endif
 
     return result;
@@ -349,7 +349,7 @@ namespace gtsam {
 #ifdef TBB_GREATER_EQUAL_2020
       result.values_.emplace(key_v.first, a * key_v.second);
 #else
-      result.values_.insert(std::make_pair(key_v.first, a * key_v.second));
+      result.values_.insert({key_v.first, a * key_v.second});
 #endif
     return result;
   }

gtsam/linear/VectorValues.h

@@ -80,7 +80,7 @@ namespace gtsam {
    public:
     typedef Values::iterator iterator;              ///< Iterator over vector values
     typedef Values::const_iterator const_iterator;  ///< Const iterator over vector values
-    typedef std::shared_ptr<This> shared_ptr;     ///< shared_ptr to this class
+    typedef std::shared_ptr<This> shared_ptr;       ///< shared_ptr to this class
     typedef Values::value_type value_type;          ///< Typedef to pair<Key, Vector>
     typedef value_type KeyValuePair;                ///< Typedef to pair<Key, Vector>
     typedef std::map<Key, size_t> Dims;             ///< Keyed vector dimensions
@@ -186,7 +186,7 @@ namespace gtsam {
 #if ! defined(GTSAM_USE_TBB) || defined (TBB_GREATER_EQUAL_2020)
       return values_.emplace(std::piecewise_construct, std::forward_as_tuple(j), std::forward_as_tuple(args...));
 #else
-      return values_.insert(std::make_pair(j, Vector(std::forward<Args>(args)...)));
+      return values_.insert({j, Vector(std::forward<Args>(args)...)});
 #endif
     }
 
@@ -195,7 +195,7 @@ namespace gtsam {
      * @param value The vector to be inserted.
      * @param j The index with which the value will be associated. */
     iterator insert(Key j, const Vector& value) {
-      return insert(std::make_pair(j, value));
+      return insert({j, value});
     }
 
     /** Insert all values from \c values.  Throws an invalid_argument exception if any keys to be
@@ -210,7 +210,7 @@ namespace gtsam {
 #ifdef TBB_GREATER_EQUAL_2020
       return values_.emplace(j, value);
 #else
-      return values_.insert(std::make_pair(j, value));
+      return values_.insert({j, value});
 #endif
     }
 

gtsam/linear/tests/testGaussianBayesNet.cpp

@@ -50,8 +50,7 @@ static GaussianBayesNet noisyBayesNet = {
 /* ************************************************************************* */
 TEST( GaussianBayesNet, Matrix )
 {
-  Matrix R; Vector d;
+  const auto [R, d] = smallBayesNet.matrix(); // find matrix and RHS
-  std::tie(R,d) = smallBayesNet.matrix(); // find matrix and RHS
 
   Matrix R1 = (Matrix2() <<
           1.0, 1.0,
@@ -100,8 +99,7 @@ TEST(GaussianBayesNet, Evaluate2) {
 /* ************************************************************************* */
 TEST( GaussianBayesNet, NoisyMatrix )
 {
-  Matrix R; Vector d;
+  const auto [R, d] = noisyBayesNet.matrix(); // find matrix and RHS
-  std::tie(R,d) = noisyBayesNet.matrix(); // find matrix and RHS
 
   Matrix R1 = (Matrix2() <<
           0.5, 0.5,
@@ -123,9 +121,7 @@ TEST(GaussianBayesNet, Optimize) {
 
 /* ************************************************************************* */
 TEST(GaussianBayesNet, NoisyOptimize) {
-  Matrix R;
+  const auto [R, d] = noisyBayesNet.matrix();  // find matrix and RHS
-  Vector d;
-  std::tie(R, d) = noisyBayesNet.matrix();  // find matrix and RHS
   const Vector x = R.inverse() * d;
   const VectorValues expected{{_x_, x.head(1)}, {_y_, x.tail(1)}};
 
@@ -236,9 +232,7 @@ TEST( GaussianBayesNet, MatrixStress )
         KeyVector({_y_, _x_, _z_}), KeyVector({_y_, _z_, _x_}),
         KeyVector({_z_, _x_, _y_}), KeyVector({_z_, _y_, _x_})}) {
     const Ordering ordering(keys);
-    Matrix R;
+    const auto [R, d] = bn.matrix(ordering);
-    Vector d;
-    std::tie(R, d) = bn.matrix(ordering);
     EXPECT(assert_equal(expected.vector(ordering), R.inverse() * d));
   }
 }

gtsam/linear/tests/testGaussianFactorGraph.cpp

@@ -155,20 +155,16 @@ TEST(GaussianFactorGraph, matrices) {
   // jacobian
   Matrix A = Ab.leftCols(Ab.cols() - 1);
   Vector b = Ab.col(Ab.cols() - 1);
-  Matrix actualA;
+  const auto [actualA, actualb] = gfg.jacobian();
-  Vector actualb;
-  std::tie(actualA, actualb) = gfg.jacobian();
   EXPECT(assert_equal(A, actualA));
   EXPECT(assert_equal(b, actualb));
 
   // hessian
   Matrix L = A.transpose() * A;
   Vector eta = A.transpose() * b;
-  Matrix actualL;
+  const auto [actualL, actualEta] = gfg.hessian();
-  Vector actualeta;
-  std::tie(actualL, actualeta) = gfg.hessian();
   EXPECT(assert_equal(L, actualL));
-  EXPECT(assert_equal(eta, actualeta));
+  EXPECT(assert_equal(eta, actualEta));
 
   // hessianBlockDiagonal
   VectorValues expectLdiagonal;  // Make explicit that diagonal is sum-squares of columns
@@ -245,9 +241,7 @@ TEST(GaussianFactorGraph, eliminate_empty) {
   // eliminate an empty factor
   GaussianFactorGraph gfg;
   gfg.add(JacobianFactor());
-  GaussianBayesNet::shared_ptr actualBN;
+  const auto [actualBN, remainingGFG] = gfg.eliminatePartialSequential(Ordering());
-  GaussianFactorGraph::shared_ptr remainingGFG;
-  std::tie(actualBN, remainingGFG) = gfg.eliminatePartialSequential(Ordering());
 
   // expected Bayes net is empty
   GaussianBayesNet expectedBN;
@@ -263,12 +257,8 @@ TEST(GaussianFactorGraph, eliminate_empty) {
 /* ************************************************************************* */
 TEST(GaussianFactorGraph, matrices2) {
   GaussianFactorGraph gfg = createSimpleGaussianFactorGraph();
-  Matrix A;
+  const auto [A, b] = gfg.jacobian();
-  Vector b;
+  const auto [AtA, eta] = gfg.hessian();
-  std::tie(A, b) = gfg.jacobian();
-  Matrix AtA;
-  Vector eta;
-  std::tie(AtA, eta) = gfg.hessian();
   EXPECT(assert_equal(A.transpose() * A, AtA));
   EXPECT(assert_equal(A.transpose() * b, eta));
   Matrix expectedAtA(6, 6);
@@ -314,9 +304,7 @@ TEST(GaussianFactorGraph, multiplyHessianAdd2) {
   GaussianFactorGraph gfg = createGaussianFactorGraphWithHessianFactor();
 
   // brute force
-  Matrix AtA;
+  const auto [AtA, eta] = gfg.hessian();
-  Vector eta;
-  std::tie(AtA, eta) = gfg.hessian();
   Vector X(6);
   X << 1, 2, 3, 4, 5, 6;
   Vector Y(6);
@@ -341,12 +329,8 @@ TEST(GaussianFactorGraph, multiplyHessianAdd2) {
 /* ************************************************************************* */
 TEST(GaussianFactorGraph, matricesMixed) {
   GaussianFactorGraph gfg = createGaussianFactorGraphWithHessianFactor();
-  Matrix A;
+  const auto [A, b] = gfg.jacobian();  // incorrect !
-  Vector b;
+  const auto [AtA, eta] = gfg.hessian();  // correct
-  std::tie(A, b) = gfg.jacobian();  // incorrect !
-  Matrix AtA;
-  Vector eta;
-  std::tie(AtA, eta) = gfg.hessian();  // correct
   EXPECT(assert_equal(A.transpose() * A, AtA));
   Vector expected = -(Vector(6) << -25, 17.5, 5, -13.5, 29, 4).finished();
   EXPECT(assert_equal(expected, eta));

gtsam/linear/tests/testHessianFactor.cpp

@@ -293,15 +293,11 @@ TEST(HessianFactor, CombineAndEliminate1) {
 
   // perform elimination on jacobian
   Ordering ordering {1};
-  GaussianConditional::shared_ptr expectedConditional;
+  const auto [expectedConditional, expectedFactor] = jacobian.eliminate(ordering);
-  JacobianFactor::shared_ptr expectedFactor;
-  std::tie(expectedConditional, expectedFactor) = jacobian.eliminate(ordering);
   CHECK(expectedFactor);
 
   // Eliminate
-  GaussianConditional::shared_ptr actualConditional;
+  const auto [actualConditional, actualHessian] = //
-  HessianFactor::shared_ptr actualHessian;
-  std::tie(actualConditional, actualHessian) = //
       EliminateCholesky(gfg, ordering);
   actualConditional->setModel(false,Vector3(1,1,1)); // add a unit model for comparison
 
@@ -356,15 +352,11 @@ TEST(HessianFactor, CombineAndEliminate2) {
 
   // perform elimination on jacobian
   Ordering ordering {0};
-  GaussianConditional::shared_ptr expectedConditional;
+  const auto [expectedConditional, expectedFactor] =
-  JacobianFactor::shared_ptr expectedFactor;
-  std::tie(expectedConditional, expectedFactor) = //
       jacobian.eliminate(ordering);
 
   // Eliminate
-  GaussianConditional::shared_ptr actualConditional;
+  const auto [actualConditional, actualHessian] = //
-  HessianFactor::shared_ptr actualHessian;
-  std::tie(actualConditional, actualHessian) = //
       EliminateCholesky(gfg, ordering);
   actualConditional->setModel(false,Vector3(1,1,1)); // add a unit model for comparison
 
@@ -498,7 +490,7 @@ TEST(HessianFactor, gradientAtZero)
 
   // test gradient at zero
   VectorValues expectedG{{0, -g1}, {1, -g2}};
-  Matrix A; Vector b; std::tie(A,b) = factor.jacobian();
+  const auto [A, b] = factor.jacobian();
   KeyVector keys {0, 1};
   EXPECT(assert_equal(-A.transpose()*b, expectedG.vector(keys)));
   VectorValues actualG = factor.gradientAtZero();

gtsam/linear/tests/testJacobianFactor.cpp

@@ -368,7 +368,7 @@ TEST(JacobianFactor, operators )
   EXPECT(assert_equal(expectedX, actualX));
 
   // test gradient at zero
-  Matrix A; Vector b2; std::tie(A,b2) = lf.jacobian();
+  const auto [A, b2] = lf.jacobian();
   VectorValues expectedG;
   expectedG.insert(1, Vector2(20,-10));
   expectedG.insert(2, Vector2(-20, 10));

gtsam/navigation/tests/testGPSFactor.cpp

@@ -123,9 +123,7 @@ TEST(GPSData, init) {
   Point3 NED2(N, E, -U);
 
   // Estimate initial state
-  Pose3 T;
+  const auto [T, nV] = GPSFactor::EstimateState(t1, NED1, t2, NED2, 84831.0796);
-  Vector3 nV;
-  std::tie(T, nV) = GPSFactor::EstimateState(t1, NED1, t2, NED2, 84831.0796);
 
   // Check values values
   EXPECT(assert_equal((Vector )Vector3(29.9575, -29.0564, -1.95993), nV, 1e-4));

gtsam/nonlinear/Expression-inl.h

@@ -143,9 +143,7 @@ T Expression<T>::value(const Values& values,
     std::vector<Matrix>* H) const {
   if (H) {
     // Call private version that returns derivatives in H
-    KeyVector keys;
+    const auto [keys, dims] = keysAndDims();
-    FastVector<int> dims;
-    std::tie(keys, dims) = keysAndDims();
     return valueAndDerivatives(values, keys, dims, *H);
   } else
     // no derivatives needed, just return value
@@ -229,7 +227,7 @@ typename Expression<T>::KeysAndDims Expression<T>::keysAndDims() const {
   std::map<Key, int> map;
   dims(map);
   size_t n = map.size();
-  KeysAndDims pair = std::make_pair(KeyVector(n), FastVector<int>(n));
+  KeysAndDims pair = {KeyVector(n), FastVector<int>(n)};
   // Copy map into pair of vectors
   auto key_it = pair.first.begin();
   auto dim_it = pair.second.begin();

gtsam/nonlinear/ISAM2.cpp

@@ -321,7 +321,7 @@ void ISAM2::recalculateIncremental(const ISAM2UpdateParams& updateParams,
     const int group =
         result->observedKeys.size() < affectedFactorsVarIndex.size() ? 1 : 0;
     for (Key var : result->observedKeys)
-      constraintGroups.insert(std::make_pair(var, group));
+      constraintGroups.emplace(var, group);
   }
 
   // Remove unaffected keys from the constraints

gtsam/nonlinear/ISAM2Clique.cpp

@@ -330,9 +330,7 @@ void ISAM2Clique::addGradientAtZero(VectorValues* g) const {
   for (auto it = conditional_->begin(); it != conditional_->end(); ++it) {
     const DenseIndex dim = conditional_->getDim(it);
     const Vector contribution = gradientContribution_.segment(position, dim);
-    VectorValues::iterator values_it;
+    auto [values_it, success] = g->tryInsert(*it, contribution);
-    bool success;
-    std::tie(values_it, success) = g->tryInsert(*it, contribution);
     if (!success) values_it->second += contribution;
     position += dim;
   }

gtsam/nonlinear/NonlinearConjugateGradientOptimizer.cpp

@@ -63,9 +63,7 @@ NonlinearConjugateGradientOptimizer::System::State NonlinearConjugateGradientOpt
 }
 
 GaussianFactorGraph::shared_ptr NonlinearConjugateGradientOptimizer::iterate() {
-  Values newValues;
+  const auto [newValues, dummy] = nonlinearConjugateGradient<System, Values>(
-  int dummy;
-  std::tie(newValues, dummy) = nonlinearConjugateGradient<System, Values>(
       System(graph_), state_->values, params_, true /* single iteration */);
   state_.reset(new State(newValues, graph_.error(newValues), state_->iterations + 1));
 
@@ -76,9 +74,7 @@ GaussianFactorGraph::shared_ptr NonlinearConjugateGradientOptimizer::iterate() {
 const Values& NonlinearConjugateGradientOptimizer::optimize() {
   // Optimize until convergence
   System system(graph_);
-  Values newValues;
+  const auto [newValues, iterations] =
-  int iterations;
-  std::tie(newValues, iterations) =
       nonlinearConjugateGradient(system, state_->values, params_, false);
   state_.reset(new State(std::move(newValues), graph_.error(newValues), iterations));
   return state_->values;

gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h

@@ -159,7 +159,7 @@ std::tuple<V, int> nonlinearConjugateGradient(const S &system,
       std::cout << "Exiting, as error = " << currentError << " < "
           << params.errorTol << std::endl;
     }
-    return std::tie(initial, iteration);
+    return {initial, iteration};
   }
 
   V currentValues = initial;
@@ -217,7 +217,7 @@ std::tuple<V, int> nonlinearConjugateGradient(const S &system,
         << "nonlinearConjugateGradient: Terminating because reached maximum iterations"
         << std::endl;
 
-  return std::tie(currentValues, iteration);
+  return {currentValues, iteration};
 }
 
 } // \ namespace gtsam

gtsam/sfm/MFAS.cpp

@@ -116,7 +116,7 @@ MFAS::MFAS(const TranslationEdges& relativeTranslations,
   // Iterate over edges, obtain weights by projecting
   // their relativeTranslations along the projection direction
   for (const auto& measurement : relativeTranslations) {
-    edgeWeights_[std::make_pair(measurement.key1(), measurement.key2())] =
+    edgeWeights_[{measurement.key1(), measurement.key2()}] =
         measurement.measured().dot(projectionDirection);
   }
 }

gtsam/sfm/ShonanAveraging.cpp

@@ -179,10 +179,8 @@ ShonanAveraging<d>::createOptimizerAt(size_t p, const Values &initial) const {
 
   // Anchor prior is added here as depends on initial value (and cost is zero)
   if (parameters_.alpha > 0) {
-    size_t i;
-    Rot value;
     const size_t dim = SOn::Dimension(p);
-    std::tie(i, value) = parameters_.anchor;
+    const auto [i, value] = parameters_.anchor;
     auto model = noiseModel::Isotropic::Precision(dim, parameters_.alpha);
     graph.emplace_shared<PriorFactor<SOn>>(i, SOn::Lift(p, value.matrix()),
                                            model);
@@ -755,7 +753,7 @@ std::pair<double, Vector> ShonanAveraging<d>::computeMinEigenVector(
     const Values &values) const {
   Vector minEigenVector;
   double minEigenValue = computeMinEigenValue(values, &minEigenVector);
-  return std::make_pair(minEigenValue, minEigenVector);
+  return {minEigenValue, minEigenVector};
 }
 
 /* ************************************************************************* */
@@ -908,7 +906,7 @@ std::pair<Values, double> ShonanAveraging<d>::run(const Values &initialEstimate,
             "When using robust norm, Shonan only tests a single rank. Set pMin = pMax");
       }
       const Values SO3Values = roundSolution(Qstar);
-      return std::make_pair(SO3Values, 0);
+      return {SO3Values, 0};
     } else {
       // Check certificate of global optimality
       Vector minEigenVector;
@@ -916,7 +914,7 @@ std::pair<Values, double> ShonanAveraging<d>::run(const Values &initialEstimate,
       if (minEigenValue > parameters_.optimalityThreshold) {
         // If at global optimum, round and return solution
         const Values SO3Values = roundSolution(Qstar);
-        return std::make_pair(SO3Values, minEigenValue);
+        return {SO3Values, minEigenValue};
       }
 
       // Not at global optimimum yet, so check whether we will go to next level

gtsam/slam/JacobianFactorQR.h

@@ -44,11 +44,9 @@ public:
     //gfg.print("gfg");
 
     // eliminate the point
-    std::shared_ptr<GaussianBayesNet> bn;
-    GaussianFactorGraph::shared_ptr fg;
     KeyVector variables;
     variables.push_back(pointKey);
-    std::tie(bn, fg) = gfg.eliminatePartialSequential(variables, EliminateQR);
+    const auto [bn, fg] = gfg.eliminatePartialSequential(variables, EliminateQR);
     //fg->print("fg");
 
     JacobianFactor::operator=(JacobianFactor(*fg));

gtsam/slam/RegularImplicitSchurFactor.h

@@ -148,7 +148,7 @@ public:
   std::pair<Matrix, Vector> jacobian() const override {
     throw std::runtime_error(
         "RegularImplicitSchurFactor::jacobian non implemented");
-    return std::make_pair(Matrix(), Vector());
+    return {Matrix(), Vector()};
   }
 
   /// *Compute* full augmented information matrix

gtsam/slam/tests/testDataset.cpp

@@ -91,9 +91,7 @@ TEST( dataSet, parseEdge)
 TEST(dataSet, load2D) {
   ///< The structure where we will save the SfM data
   const string filename = findExampleDataFile("w100.graph");
-  NonlinearFactorGraph::shared_ptr graph;
+  const auto [graph, initial] = load2D(filename);
-  Values::shared_ptr initial;
-  std::tie(graph, initial) = load2D(filename);
   EXPECT_LONGS_EQUAL(300, graph->size());
   EXPECT_LONGS_EQUAL(100, initial->size());
   auto model = noiseModel::Unit::Create(3);
@@ -134,20 +132,17 @@ TEST(dataSet, load2D) {
 /* ************************************************************************* */
 TEST(dataSet, load2DVictoriaPark) {
   const string filename = findExampleDataFile("victoria_park.txt");
-  NonlinearFactorGraph::shared_ptr graph;
-  Values::shared_ptr initial;
-
   // Load all
-  std::tie(graph, initial) = load2D(filename);
+  const auto [graph1, initial1] = load2D(filename);
-  EXPECT_LONGS_EQUAL(10608, graph->size());
+  EXPECT_LONGS_EQUAL(10608, graph1->size());
-  EXPECT_LONGS_EQUAL(7120, initial->size());
+  EXPECT_LONGS_EQUAL(7120, initial1->size());
 
   // Restrict keys
   size_t maxIndex = 5;
-  std::tie(graph, initial) = load2D(filename, nullptr, maxIndex);
+  const auto [graph2, initial2] = load2D(filename, nullptr, maxIndex);
-  EXPECT_LONGS_EQUAL(5, graph->size());
+  EXPECT_LONGS_EQUAL(5, graph2->size());
-  EXPECT_LONGS_EQUAL(6, initial->size()); // file has 0 as well
+  EXPECT_LONGS_EQUAL(6, initial2->size()); // file has 0 as well
-  EXPECT_LONGS_EQUAL(L(5), graph->at(4)->keys()[1]);
+  EXPECT_LONGS_EQUAL(L(5), graph2->at(4)->keys()[1]);
 }
 
 /* ************************************************************************* */
@@ -217,10 +212,8 @@ TEST(dataSet, readG2o3D) {
   }
 
   // Check graph version
-  NonlinearFactorGraph::shared_ptr actualGraph;
-  Values::shared_ptr actualValues;
   bool is3D = true;
-  std::tie(actualGraph, actualValues) = readG2o(g2oFile, is3D);
+  const auto [actualGraph, actualValues] = readG2o(g2oFile, is3D);
   EXPECT(assert_equal(expectedGraph, *actualGraph, 1e-5));
   for (size_t j : {0, 1, 2, 3, 4}) {
     EXPECT(assert_equal(poses[j], actualValues->at<Pose3>(j), 1e-5));
@@ -231,10 +224,8 @@ TEST(dataSet, readG2o3D) {
 TEST( dataSet, readG2o3DNonDiagonalNoise)
 {
   const string g2oFile = findExampleDataFile("pose3example-offdiagonal.txt");
-  NonlinearFactorGraph::shared_ptr actualGraph;
-  Values::shared_ptr actualValues;
   bool is3D = true;
-  std::tie(actualGraph, actualValues) = readG2o(g2oFile, is3D);
+  const auto [actualGraph, actualValues] = readG2o(g2oFile, is3D);
 
   Values expectedValues;
   Rot3 R0 = Rot3::Quaternion(1.000000, 0.000000, 0.000000, 0.000000 );
@@ -326,9 +317,7 @@ static NonlinearFactorGraph expectedGraph(const SharedNoiseModel& model) {
 /* ************************************************************************* */
 TEST(dataSet, readG2o) {
   const string g2oFile = findExampleDataFile("pose2example");
-  NonlinearFactorGraph::shared_ptr actualGraph;
+  const auto [actualGraph, actualValues] = readG2o(g2oFile);
-  Values::shared_ptr actualValues;
-  std::tie(actualGraph, actualValues) = readG2o(g2oFile);
 
   auto model = noiseModel::Diagonal::Precisions(
       Vector3(44.721360, 44.721360, 30.901699));
@@ -352,10 +341,8 @@ TEST(dataSet, readG2o) {
 /* ************************************************************************* */
 TEST(dataSet, readG2oHuber) {
   const string g2oFile = findExampleDataFile("pose2example");
-  NonlinearFactorGraph::shared_ptr actualGraph;
-  Values::shared_ptr actualValues;
   bool is3D = false;
-  std::tie(actualGraph, actualValues) =
+  const auto [actualGraph, actualValues] =
       readG2o(g2oFile, is3D, KernelFunctionTypeHUBER);
 
   auto baseModel = noiseModel::Diagonal::Precisions(
@@ -369,10 +356,8 @@ TEST(dataSet, readG2oHuber) {
 /* ************************************************************************* */
 TEST(dataSet, readG2oTukey) {
   const string g2oFile = findExampleDataFile("pose2example");
-  NonlinearFactorGraph::shared_ptr actualGraph;
-  Values::shared_ptr actualValues;
   bool is3D = false;
-  std::tie(actualGraph, actualValues) =
+  const auto [actualGraph, actualValues] =
       readG2o(g2oFile, is3D, KernelFunctionTypeTUKEY);
 
   auto baseModel = noiseModel::Diagonal::Precisions(
@@ -387,16 +372,12 @@ TEST(dataSet, readG2oTukey) {
 TEST( dataSet, writeG2o)
 {
   const string g2oFile = findExampleDataFile("pose2example");
-  NonlinearFactorGraph::shared_ptr expectedGraph;
+  const auto [expectedGraph, expectedValues] = readG2o(g2oFile);
-  Values::shared_ptr expectedValues;
-  std::tie(expectedGraph, expectedValues) = readG2o(g2oFile);
 
   const string filenameToWrite = createRewrittenFileName(g2oFile);
   writeG2o(*expectedGraph, *expectedValues, filenameToWrite);
 
-  NonlinearFactorGraph::shared_ptr actualGraph;
+  const auto [actualGraph, actualValues] = readG2o(filenameToWrite);
-  Values::shared_ptr actualValues;
-  std::tie(actualGraph, actualValues) = readG2o(filenameToWrite);
   EXPECT(assert_equal(*expectedValues,*actualValues,1e-5));
   EXPECT(assert_equal(*expectedGraph,*actualGraph,1e-5));
 }
@@ -405,17 +386,13 @@ TEST( dataSet, writeG2o)
 TEST( dataSet, writeG2o3D)
 {
   const string g2oFile = findExampleDataFile("pose3example");
-  NonlinearFactorGraph::shared_ptr expectedGraph;
-  Values::shared_ptr expectedValues;
   bool is3D = true;
-  std::tie(expectedGraph, expectedValues) = readG2o(g2oFile, is3D);
+  const auto [expectedGraph, expectedValues] = readG2o(g2oFile, is3D);
 
   const string filenameToWrite = createRewrittenFileName(g2oFile);
   writeG2o(*expectedGraph, *expectedValues, filenameToWrite);
 
-  NonlinearFactorGraph::shared_ptr actualGraph;
+  const auto [actualGraph, actualValues] = readG2o(filenameToWrite, is3D);
-  Values::shared_ptr actualValues;
-  std::tie(actualGraph, actualValues) = readG2o(filenameToWrite, is3D);
   EXPECT(assert_equal(*expectedValues,*actualValues,1e-4));
   EXPECT(assert_equal(*expectedGraph,*actualGraph,1e-4));
 }
@@ -424,17 +401,13 @@ TEST( dataSet, writeG2o3D)
 TEST( dataSet, writeG2o3DNonDiagonalNoise)
 {
   const string g2oFile = findExampleDataFile("pose3example-offdiagonal");
-  NonlinearFactorGraph::shared_ptr expectedGraph;
-  Values::shared_ptr expectedValues;
   bool is3D = true;
-  std::tie(expectedGraph, expectedValues) = readG2o(g2oFile, is3D);
+  const auto [expectedGraph, expectedValues] = readG2o(g2oFile, is3D);
 
   const string filenameToWrite = createRewrittenFileName(g2oFile);
   writeG2o(*expectedGraph, *expectedValues, filenameToWrite);
 
-  NonlinearFactorGraph::shared_ptr actualGraph;
+  const auto [actualGraph, actualValues] = readG2o(filenameToWrite, is3D);
-  Values::shared_ptr actualValues;
-  std::tie(actualGraph, actualValues) = readG2o(filenameToWrite, is3D);
   EXPECT(assert_equal(*expectedValues,*actualValues,1e-4));
   EXPECT(assert_equal(*expectedGraph,*actualGraph,1e-4));
 }

gtsam/slam/tests/testInitializePose.cpp

@@ -33,10 +33,8 @@ using namespace gtsam;
 /* ************************************************************************* */
 TEST(InitializePose3, computePoses2D) {
   const string g2oFile = findExampleDataFile("noisyToyGraph.txt");
-  NonlinearFactorGraph::shared_ptr inputGraph;
-  Values::shared_ptr posesInFile;
   bool is3D = false;
-  std::tie(inputGraph, posesInFile) = readG2o(g2oFile, is3D);
+  const auto [inputGraph, posesInFile] = readG2o(g2oFile, is3D);
 
   auto priorModel = noiseModel::Unit::Create(3);
   inputGraph->addPrior(0, posesInFile->at<Pose2>(0), priorModel);
@@ -56,10 +54,8 @@ TEST(InitializePose3, computePoses2D) {
 /* ************************************************************************* */
 TEST(InitializePose3, computePoses3D) {
   const string g2oFile = findExampleDataFile("Klaus3");
-  NonlinearFactorGraph::shared_ptr inputGraph;
-  Values::shared_ptr posesInFile;
   bool is3D = true;
-  std::tie(inputGraph, posesInFile) = readG2o(g2oFile, is3D);
+  const auto [inputGraph, posesInFile] = readG2o(g2oFile, is3D);
 
   auto priorModel = noiseModel::Unit::Create(6);
   inputGraph->addPrior(0, posesInFile->at<Pose3>(0), priorModel);

gtsam/slam/tests/testInitializePose3.cpp

@@ -231,10 +231,8 @@ TEST( InitializePose3, orientationsGradient ) {
   //  writeG2o(pose3Graph, givenPoses, g2oFile);
 
   const string matlabResultsfile = findExampleDataFile("simpleGraph10gradIter");
-  NonlinearFactorGraph::shared_ptr matlabGraph;
-  Values::shared_ptr matlabValues;
   bool is3D = true;
-  std::tie(matlabGraph, matlabValues) = readG2o(matlabResultsfile, is3D);
+  const auto [matlabGraph, matlabValues] = readG2o(matlabResultsfile, is3D);
 
   Rot3 R0Expected = matlabValues->at<Pose3>(1).rotation();
   EXPECT(assert_equal(R0Expected, orientations.at<Rot3>(x0), 1e-4));
@@ -266,10 +264,8 @@ TEST( InitializePose3, posesWithGivenGuess ) {
 /* ************************************************************************* */
 TEST(InitializePose3, initializePoses) {
   const string g2oFile = findExampleDataFile("pose3example-grid");
-  NonlinearFactorGraph::shared_ptr inputGraph;
-  Values::shared_ptr posesInFile;
   bool is3D = true;
-  std::tie(inputGraph, posesInFile) = readG2o(g2oFile, is3D);
+  const auto [inputGraph, posesInFile] = readG2o(g2oFile, is3D);
 
   auto priorModel = noiseModel::Unit::Create(6);
   inputGraph->addPrior(0, Pose3(), priorModel);

gtsam/slam/tests/testLago.cpp

@@ -258,9 +258,7 @@ TEST( Lago, smallGraph2 ) {
 TEST( Lago, largeGraphNoisy_orientations ) {
 
   string inputFile = findExampleDataFile("noisyToyGraph");
-  NonlinearFactorGraph::shared_ptr g;
+  const auto [g, initial] = readG2o(inputFile);
-  Values::shared_ptr initial;
-  std::tie(g, initial) = readG2o(inputFile);
 
   // Add prior on the pose having index (key) = 0
   NonlinearFactorGraph graphWithPrior = *g;
@@ -279,9 +277,7 @@ TEST( Lago, largeGraphNoisy_orientations ) {
     }
   }
   string matlabFile = findExampleDataFile("orientationsNoisyToyGraph");
-  NonlinearFactorGraph::shared_ptr gmatlab;
+  const auto [gmatlab, expected] = readG2o(matlabFile);
-  Values::shared_ptr expected;
-  std::tie(gmatlab, expected) = readG2o(matlabFile);
 
   for(const auto& key_pose: expected->extract<Pose2>()){
     const Key& k = key_pose.first;
@@ -294,9 +290,7 @@ TEST( Lago, largeGraphNoisy_orientations ) {
 TEST( Lago, largeGraphNoisy ) {
 
   string inputFile = findExampleDataFile("noisyToyGraph");
-  NonlinearFactorGraph::shared_ptr g;
+  const auto [g, initial] = readG2o(inputFile);
-  Values::shared_ptr initial;
-  std::tie(g, initial) = readG2o(inputFile);
 
   // Add prior on the pose having index (key) = 0
   NonlinearFactorGraph graphWithPrior = *g;
@@ -306,9 +300,7 @@ TEST( Lago, largeGraphNoisy ) {
   Values actual = lago::initialize(graphWithPrior);
 
   string matlabFile = findExampleDataFile("optimizedNoisyToyGraph");
-  NonlinearFactorGraph::shared_ptr gmatlab;
+  const auto [gmatlab, expected] = readG2o(matlabFile);
-  Values::shared_ptr expected;
-  std::tie(gmatlab, expected) = readG2o(matlabFile);
 
   for(const auto& key_pose: expected->extract<Pose2>()){
     const Key& k = key_pose.first;

gtsam/symbolic/SymbolicFactor-inst.h

@@ -61,9 +61,10 @@ namespace gtsam
       std::set_difference(allKeys.begin(), allKeys.end(), frontals.begin(), frontals.end(), orderedKeys.begin() + nFrontals);
 
       // Return resulting conditional and factor
-      return std::make_pair(
+      return {
         SymbolicConditional::FromKeysShared(orderedKeys, nFrontals),
-        SymbolicFactor::FromIteratorsShared(orderedKeys.begin() + nFrontals, orderedKeys.end()));
+        SymbolicFactor::FromIteratorsShared(orderedKeys.begin() + nFrontals, orderedKeys.end())
+      };
     }
   }
 }

gtsam/symbolic/tests/testSymbolicFactor.cpp

@@ -74,9 +74,7 @@ TEST(SymbolicFactor, EliminateSymbolic)
   const SymbolicConditional expectedConditional =
     SymbolicConditional::FromKeys(KeyVector{0,1,2,3,4,5,6}, 4);
 
-  SymbolicFactor::shared_ptr actualFactor;
+  const auto [actualConditional, actualFactor] =
-  SymbolicConditional::shared_ptr actualConditional;
-  std::tie(actualConditional, actualFactor) =
       EliminateSymbolic(factors, Ordering{0, 1, 2, 3});
 
   CHECK(assert_equal(expectedConditional, *actualConditional));

gtsam/symbolic/tests/testSymbolicFactorGraph.cpp

@@ -65,17 +65,13 @@ TEST(SymbolicFactorGraph, eliminatePartialSequential) {
   const auto expectedSfg = SymbolicFactorGraph(SymbolicFactor(2, 3))(
       SymbolicFactor(4, 5))(SymbolicFactor(2, 3, 4));
 
-  SymbolicBayesNet::shared_ptr actualBayesNet;
+  const auto [actualBayesNet, actualSfg] =
-  SymbolicFactorGraph::shared_ptr actualSfg;
-  std::tie(actualBayesNet, actualSfg) =
       simpleTestGraph2.eliminatePartialSequential(Ordering{0, 1});
 
   EXPECT(assert_equal(expectedSfg, *actualSfg));
   EXPECT(assert_equal(expectedBayesNet, *actualBayesNet));
 
-  SymbolicBayesNet::shared_ptr actualBayesNet2;
+  const auto [actualBayesNet2, actualSfg2] =
-  SymbolicFactorGraph::shared_ptr actualSfg2;
-  std::tie(actualBayesNet2, actualSfg2) =
       simpleTestGraph2.eliminatePartialSequential(Ordering{0, 1});
 
   EXPECT(assert_equal(expectedSfg, *actualSfg2));
@@ -106,9 +102,7 @@ TEST(SymbolicFactorGraph, eliminatePartialMultifrontal) {
       SymbolicFactorGraph(SymbolicFactor(0, 1))(SymbolicFactor(0, 2))(
           SymbolicFactor(1, 3))(SymbolicFactor(2, 3))(SymbolicFactor(1));
 
-  SymbolicBayesTree::shared_ptr actualBayesTree;
+  const auto [actualBayesTree, actualFactorGraph] =
-  SymbolicFactorGraph::shared_ptr actualFactorGraph;
-  std::tie(actualBayesTree, actualFactorGraph) =
       simpleTestGraph2.eliminatePartialMultifrontal(Ordering{4, 5});
 
   EXPECT(assert_equal(expectedFactorGraph, *actualFactorGraph));
@@ -122,9 +116,7 @@ TEST(SymbolicFactorGraph, eliminatePartialMultifrontal) {
       std::make_shared<SymbolicConditional>(5, 4)));
   expectedBayesTree2.insertRoot(root2);
 
-  SymbolicBayesTree::shared_ptr actualBayesTree2;
+  const auto [actualBayesTree2, actualFactorGraph2] =
-  SymbolicFactorGraph::shared_ptr actualFactorGraph2;
-  std::tie(actualBayesTree2, actualFactorGraph2) =
       simpleTestGraph2.eliminatePartialMultifrontal(KeyVector{4, 5});
 
   EXPECT(assert_equal(expectedFactorGraph, *actualFactorGraph2));
@@ -152,11 +144,11 @@ TEST(SymbolicFactorGraph, eliminate_disconnected_graph) {
 
   // create expected Chordal bayes Net
   SymbolicBayesNet expected;
-  expected.push_back(std::make_shared<SymbolicConditional>(0, 1, 2));
+  expected.emplace_shared<SymbolicConditional>(0, 1, 2);
-  expected.push_back(std::make_shared<SymbolicConditional>(1, 2));
+  expected.emplace_shared<SymbolicConditional>(1, 2);
-  expected.push_back(std::make_shared<SymbolicConditional>(2));
+  expected.emplace_shared<SymbolicConditional>(2);
-  expected.push_back(std::make_shared<SymbolicConditional>(3, 4));
+  expected.emplace_shared<SymbolicConditional>(3, 4);
-  expected.push_back(std::make_shared<SymbolicConditional>(4));
+  expected.emplace_shared<SymbolicConditional>(4);
 
   Ordering order;
   order += 0, 1, 2, 3, 4;

gtsam_unstable/discrete/examples/schedulingQuals12.cpp

@@ -176,9 +176,6 @@ void solveStaged(size_t addMutex = 2) {
     gttoc_(eliminate);
 
     // find root node
-//    chordal->back()->print("back: ");
-//    chordal->front()->print("front: ");
-//    exit(0);
     DiscreteConditional::shared_ptr root = chordal->back();
     if (debug)
       root->print(""/*scheduler.studentName(s)*/);
@@ -210,7 +207,6 @@ DiscreteBayesNet::shared_ptr createSampler(size_t i,
   SETDEBUG("Scheduler::buildGraph", false);
   scheduler.addStudentSpecificConstraints(0, slot);
   DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate();
-  // chordal->print(scheduler[i].studentKey(0).name()); // large !
   schedulers.push_back(scheduler);
   return chordal;
 }

gtsam_unstable/discrete/tests/testLoopyBelief.cpp

@@ -79,8 +79,6 @@ class LoopyBelief {
   DiscreteFactorGraph::shared_ptr iterate(
       const std::map<Key, DiscreteKey>& allDiscreteKeys) {
     static const bool debug = false;
-    static DiscreteConditional::shared_ptr
-        dummyCond;  // unused by-product of elimination
     DiscreteFactorGraph::shared_ptr beliefs(new DiscreteFactorGraph());
     std::map<Key, std::map<Key, DiscreteFactor::shared_ptr> > allMessages;
     // Eliminate each star graph
@@ -99,8 +97,7 @@ class LoopyBelief {
           subGraph.push_back(starGraphs_.at(key).star->at(factor));
         }
         if (debug) subGraph.print("------- Subgraph:");
-        DiscreteFactor::shared_ptr message;
+        const auto [dummyCond, message] =
-        std::tie(dummyCond, message) =
             EliminateDiscrete(subGraph, Ordering{neighbor});
         // store the new factor into messages
         messages.insert(make_pair(neighbor, message));

gtsam_unstable/geometry/tests/testInvDepthCamera3.cpp

@@ -130,9 +130,7 @@ TEST(InvDepthFactor, backproject)
   InvDepthCamera3<Cal3_S2> inv_camera(level_pose,K);
   Point2 z = inv_camera.project(expected, inv_depth);
 
-  Vector5 actual_vec;
+  const auto [actual_vec, actual_inv] = inv_camera.backproject(z, 4);
-  double actual_inv;
-  std::tie(actual_vec, actual_inv) = inv_camera.backproject(z, 4);
   EXPECT(assert_equal(expected,actual_vec,1e-7));
   EXPECT_DOUBLES_EQUAL(inv_depth,actual_inv,1e-7);
 }
@@ -146,9 +144,7 @@ TEST(InvDepthFactor, backproject2)
   InvDepthCamera3<Cal3_S2> inv_camera(Pose3(Rot3::Ypr(1.5,0.1, -1.5), Point3(-5, -5, 2)),K);
   Point2 z = inv_camera.project(expected, inv_depth);
 
-  Vector5 actual_vec;
+  const auto [actual_vec, actual_inv] = inv_camera.backproject(z, 10);
-  double actual_inv;
-  std::tie(actual_vec, actual_inv) = inv_camera.backproject(z, 10);
   EXPECT(assert_equal(expected,actual_vec,1e-7));
   EXPECT_DOUBLES_EQUAL(inv_depth,actual_inv,1e-7);
 }

gtsam_unstable/linear/ActiveSetSolver-inl.h

@@ -219,10 +219,8 @@ Template typename This::State This::iterate(
   } else {
     // If we CAN make some progress, i.e. p_k != 0
     // Adapt stepsize if some inactive constraints complain about this move
-    double alpha;
-    int factorIx;
     VectorValues p = newValues - state.values;
-    std::tie(alpha, factorIx) = // using 16.41
+    const auto [alpha, factorIx] = // using 16.41
         computeStepSize(state.workingSet, state.values, p, POLICY::maxAlpha);
     // also add to the working set the one that complains the most
     InequalityFactorGraph newWorkingSet = state.workingSet;

gtsam_unstable/linear/tests/testLPSolver.cpp

@@ -69,8 +69,7 @@ TEST(LPInitSolver, InfiniteLoopSingleVar) {
   LPSolver solver(lp);
   VectorValues starter;
   starter.insert(1, Vector3(0, 0, 2));
-  VectorValues results, duals;
+  const auto [results, duals] = solver.optimize(starter);
-  std::tie(results, duals) = solver.optimize(starter);
   VectorValues expected;
   expected.insert(1, Vector3(13.5, 6.5, -6.5));
   CHECK(assert_equal(results, expected, 1e-7));
@@ -97,8 +96,7 @@ TEST(LPInitSolver, InfiniteLoopMultiVar) {
   starter.insert(X, kZero);
   starter.insert(Y, kZero);
   starter.insert(Z, Vector::Constant(1, 2.0));
-  VectorValues results, duals;
+  const auto [results, duals] = solver.optimize(starter);
-  std::tie(results, duals) = solver.optimize(starter);
   VectorValues expected;
   expected.insert(X, Vector::Constant(1, 13.5));
   expected.insert(Y, Vector::Constant(1, 6.5));
@@ -197,8 +195,7 @@ TEST(LPSolver, SimpleTest1) {
   expected_x1.insert(1, Vector::Ones(2));
   CHECK(assert_equal(expected_x1, x1, 1e-10));
 
-  VectorValues result, duals;
+  const auto [result, duals] = lpSolver.optimize(init);
-  std::tie(result, duals) = lpSolver.optimize(init);
   VectorValues expectedResult;
   expectedResult.insert(1, Vector2(8. / 3., 2. / 3.));
   CHECK(assert_equal(expectedResult, result, 1e-10));
@@ -208,9 +205,9 @@ TEST(LPSolver, SimpleTest1) {
 TEST(LPSolver, TestWithoutInitialValues) {
   LP lp = simpleLP1();
   LPSolver lpSolver(lp);
-  VectorValues result, duals, expectedResult;
+  VectorValues expectedResult;
   expectedResult.insert(1, Vector2(8. / 3., 2. / 3.));
-  std::tie(result, duals) = lpSolver.optimize();
+  const auto [result, duals] = lpSolver.optimize();
   CHECK(assert_equal(expectedResult, result));
 }
 

gtsam_unstable/linear/tests/testLinearEquality.cpp

@@ -200,9 +200,7 @@ TEST(LinearEquality, operators) {
   EXPECT(assert_equal(expectedX, actualX));
 
   // test gradient at zero
-  Matrix A;
+  const auto [A, b2] = lf.jacobian();
-  Vector b2;
-  std::tie(A, b2) = lf.jacobian();
   VectorValues expectedG;
   expectedG.insert(1, (Vector(2) << 0.2, -0.1).finished());
   expectedG.insert(2, (Vector(2) << -0.2, 0.1).finished());

gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherDL.cpp

@@ -584,10 +584,10 @@ TEST( ConcurrentIncrementalSmootherDL, synchronize_3 )
     allkeys.erase(key);
   }
   KeyVector variables(allkeys.begin(), allkeys.end());
-  std::pair<GaussianBayesNet::shared_ptr, GaussianFactorGraph::shared_ptr> result = LinFactorGraph->eliminatePartialSequential(variables, EliminateCholesky);
+  const auto [bn, fg] = LinFactorGraph->eliminatePartialSequential(variables, EliminateCholesky);
 
   expectedSmootherSummarization.resize(0);
-  for(const GaussianFactor::shared_ptr& factor: *result.second) {
+  for(const GaussianFactor::shared_ptr& factor: *fg) {
     expectedSmootherSummarization.push_back(LinearContainerFactor(factor, allValues));
   }
 

gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherGN.cpp

@@ -584,10 +584,10 @@ TEST( ConcurrentIncrementalSmootherGN, synchronize_3 )
   KeySet allkeys = LinFactorGraph->keys();
   for (const auto key : filterSeparatorValues.keys()) allkeys.erase(key);
   KeyVector variables(allkeys.begin(), allkeys.end());
-  std::pair<GaussianBayesNet::shared_ptr, GaussianFactorGraph::shared_ptr> result = LinFactorGraph->eliminatePartialSequential(variables, EliminateCholesky);
+  const auto [bn, fg] = LinFactorGraph->eliminatePartialSequential(variables, EliminateCholesky);
 
   expectedSmootherSummarization.resize(0);
-  for(const GaussianFactor::shared_ptr& factor: *result.second) {
+  for(const GaussianFactor::shared_ptr& factor: *fg) {
     expectedSmootherSummarization.push_back(LinearContainerFactor(factor, allValues));
   }
 

gtsam_unstable/nonlinear/tests/testNonlinearClusterTree.cpp

@@ -86,8 +86,8 @@ TEST(NonlinearClusterTree, Clusters) {
   // Calculate expected result of only evaluating the marginalCluster
   Ordering ordering;
   ordering.push_back(x1);
-  auto expected = gfg->eliminatePartialSequential(ordering);
+  const auto [bn, fg] = gfg->eliminatePartialSequential(ordering);
-  auto expectedFactor = std::dynamic_pointer_cast<HessianFactor>(expected.second->at(0));
+  auto expectedFactor = std::dynamic_pointer_cast<HessianFactor>(fg->at(0));
   if (!expectedFactor)
     throw std::runtime_error("Expected HessianFactor");
 
@@ -95,7 +95,7 @@ TEST(NonlinearClusterTree, Clusters) {
   auto actual = marginalCluster->linearizeAndEliminate(initial);
   const GaussianBayesNet& bayesNet = actual.first;
   const HessianFactor& factor = *actual.second;
-  EXPECT(assert_equal(*expected.first->at(0), *bayesNet.at(0), 1e-6));
+  EXPECT(assert_equal(*bn->at(0), *bayesNet.at(0), 1e-6));
   EXPECT(assert_equal(*expectedFactor, factor, 1e-6));
 }
 

gtsam_unstable/partition/FindSeparator-inl.h

@@ -249,9 +249,7 @@ namespace gtsam { namespace partition {
     prepareMetisGraph<GenericGraph>(graph, keys, workspace, &xadj, &adjncy, &adjwgt);
 
     // run ND on the graph
-    size_t sepsize;
+    const auto [sepsize, part] = separatorMetis(numKeys, xadj, adjncy, adjwgt, verbose);
-    sharedInts part;
-    std::tie(sepsize, part) = separatorMetis(numKeys, xadj, adjncy, adjwgt, verbose);
     if (!sepsize)  return std::optional<MetisResult>();
 
     // convert the 0-1-2 from Metis to 1-2-0, so that the separator is 0, as later
@@ -309,9 +307,7 @@ namespace gtsam { namespace partition {
     prepareMetisGraph<GenericGraph>(graph, keys, workspace, &xadj, &adjncy, &adjwgt);
 
     // run metis on the graph
-    int edgecut;
+    const auto [edgecut, part] = edgeMetis(numKeys, xadj, adjncy, adjwgt, verbose);
-    sharedInts part;
-    std::tie(edgecut, part) = edgeMetis(numKeys, xadj, adjncy, adjwgt, verbose);
 
     // convert the 0-1-2 from Metis to 1-2-0, so that the separator is 0, as later we will have more submaps
     MetisResult result;

gtsam_unstable/partition/NestedDissection-inl.h

@@ -20,9 +20,7 @@ namespace gtsam { namespace partition {
   NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection(
       const NLG& fg, const Ordering& ordering, const int numNodeStopPartition, const int minNodesPerMap, const bool verbose) :
   fg_(fg), ordering_(ordering){
-    GenericUnaryGraph unaryFactors;
+    const auto [unaryFactors, gfg] = fg.createGenericGraph(ordering);
-    GenericGraph gfg;
-    std::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering);
 
     // build reverse mapping from integer to symbol
     int numNodes = ordering.size();
@@ -44,9 +42,7 @@ namespace gtsam { namespace partition {
   template <class NLG, class SubNLG, class GenericGraph>
   NestedDissection<NLG, SubNLG, GenericGraph>::NestedDissection(
       const NLG& fg, const Ordering& ordering, const std::shared_ptr<Cuts>& cuts, const bool verbose) : fg_(fg), ordering_(ordering){
-    GenericUnaryGraph unaryFactors;
+    const auto [unaryFactors, gfg] = fg.createGenericGraph(ordering);
-    GenericGraph gfg;
-    std::tie(unaryFactors, gfg) = fg.createGenericGraph(ordering);
 
     // build reverse mapping from integer to symbol
     int numNodes = ordering.size();

gtsam_unstable/slam/tests/testAHRS.cpp

@@ -70,17 +70,15 @@ TEST (AHRS, constructor) {
 
 /* ************************************************************************* */
 // TODO make a testMechanization_bRn2
-TEST (AHRS, Mechanization_integrate) {
+TEST(AHRS, Mechanization_integrate) {
-  AHRS ahrs = AHRS(stationaryU,stationaryF,g_e);
+  AHRS ahrs = AHRS(stationaryU, stationaryF, g_e);
-  Mechanization_bRn2 mech;
+  // const auto [mech, state] = ahrs.initialize(g_e);
-  KalmanFilter::State state;
+  // Vector u = Vector3(0.05, 0.0, 0.0);
-//  std::tie(mech,state) = ahrs.initialize(g_e);
+  // double dt = 2;
-//  Vector u = Vector3(0.05,0.0,0.0);
+  // Rot3 expected;
-//  double dt = 2;
+  // Mechanization_bRn2 mech2 = mech.integrate(u, dt);
-//  Rot3 expected;
+  // Rot3 actual = mech2.bRn();
-//  Mechanization_bRn2 mech2 = mech.integrate(u,dt);
+  // EXPECT(assert_equal(expected, actual));
-//  Rot3 actual = mech2.bRn();
-//  EXPECT(assert_equal(expected, actual));
 }
 
 /* ************************************************************************* */

gtsam_unstable/slam/tests/testInvDepthFactor3.cpp

@@ -118,10 +118,8 @@ TEST( InvDepthFactor, Jacobian3D ) {
   Point2 expected_uv = level_camera.project(landmark);
 
   // get expected landmark representation using backprojection
-  double inv_depth;
-  Vector5 inv_landmark;
   InvDepthCamera3<Cal3_S2> inv_camera(level_pose, K);
-  std::tie(inv_landmark, inv_depth) = inv_camera.backproject(expected_uv, 5);
+  const auto [inv_landmark, inv_depth] = inv_camera.backproject(expected_uv, 5);
   Vector5 expected_inv_landmark((Vector(5) << 0., 0., 1., 0., 0.).finished());
 
   CHECK(assert_equal(expected_inv_landmark, inv_landmark, 1e-6));

package.xml

@@ -6,8 +6,17 @@
   <description>gtsam</description>
 
   <maintainer email="gtsam@lists.gatech.edu">Frank Dellaert</maintainer>
-  <license>BSD</license>
+
-  
+  <!-- Main License for GTSAM sources -->
+  <license>BSD-3-Clause</license>
+  <!-- 3rd party libraries -->
+  <license file="gtsam/3rdparty/CCOLAMD/Doc/License.txt">BSD-3-Clause</license>
+  <license file="gtsam/3rdparty/ceres/jet.h">BSD-3-Clause</license>
+  <license file="gtsam/3rdparty/Eigen/COPYING.README">MPL-2.0</license>
+  <license file="gtsam/3rdparty/GeographicLib/LICENSE.txt">MIT</license>
+  <license file="gtsam/3rdparty/metis/LICENSE.txt">Apache-2.0</license>
+  <license file="gtsam/3rdparty/Spectra/GenEigsSolver.h">MPL-2.0</license>
+
   <!-- Maintainers of the ROS packaging -->
   <maintainer email="fan.jiang@gatech.edu">Fan Jiang</maintainer>
   <maintainer email="jlblanco@ual.es">José Luis Blanco-Claraco</maintainer>

tests/smallExample.h

@@ -463,10 +463,7 @@ inline std::pair<NonlinearFactorGraph, Values> createNonlinearSmoother(int T) {
 
 /* ************************************************************************* */
 inline GaussianFactorGraph createSmoother(int T) {
-  NonlinearFactorGraph nlfg;
+  const auto [nlfg, poses] = createNonlinearSmoother(T);
-  Values poses;
-  std::tie(nlfg, poses) = createNonlinearSmoother(T);
-
   return *nlfg.linearize(poses);
 }
 

tests/testExpressionFactor.cpp

@@ -233,9 +233,7 @@ TEST(ExpressionFactor, Shallow) {
   Point2_ expression = project(transformTo(x_, p_));
 
   // Get and check keys and dims
-  KeyVector keys;
+  const auto [keys, dims] = expression.keysAndDims();
-  FastVector<int> dims;
-  std::tie(keys, dims) = expression.keysAndDims();
   LONGS_EQUAL(2,keys.size());
   LONGS_EQUAL(2,dims.size());
   LONGS_EQUAL(1,keys[0]);

tests/testGaussianFactorGraphB.cpp

@@ -111,9 +111,7 @@ TEST(GaussianFactorGraph, eliminateOne_l1) {
 /* ************************************************************************* */
 TEST(GaussianFactorGraph, eliminateOne_x1_fast) {
   GaussianFactorGraph fg = createGaussianFactorGraph();
-  GaussianConditional::shared_ptr conditional;
+  const auto [conditional, remaining] = EliminateQR(fg, Ordering{X(1)});
-  JacobianFactor::shared_ptr remaining;
-  std::tie(conditional, remaining) = EliminateQR(fg, Ordering{X(1)});
 
   // create expected Conditional Gaussian
   Matrix I = 15 * I_2x2, R11 = I, S12 = -0.111111 * I, S13 = -0.444444 * I;
@@ -289,9 +287,7 @@ TEST(GaussianFactorGraph, elimination) {
   GaussianBayesNet bayesNet = *fg.eliminateSequential();
 
   // Check matrix
-  Matrix R;
+  const auto [R, d] = bayesNet.matrix();
-  Vector d;
-  std::tie(R, d) = bayesNet.matrix();
   Matrix expected =
       (Matrix(2, 2) << 0.707107, -0.353553, 0.0, 0.612372).finished();
   Matrix expected2 =

tests/testGaussianISAM2.cpp

@@ -246,7 +246,6 @@ bool isam_check(const NonlinearFactorGraph& fullgraph, const Values& fullinit, c
 
   // Check gradient at each node
   bool nodeGradientsOk = true;
-  typedef ISAM2::sharedClique sharedClique;
   for (const auto& [key, clique] : isam.nodes()) {
     // Compute expected gradient
     GaussianFactorGraph jfg;
@@ -450,7 +449,6 @@ TEST(ISAM2, swapFactors)
   EXPECT(isam_check(fullgraph, fullinit, isam, *this, result_));
 
   // Check gradient at each node
-  typedef ISAM2::sharedClique sharedClique;
   for (const auto& [key, clique]: isam.nodes()) {
     // Compute expected gradient
     GaussianFactorGraph jfg;
@@ -575,7 +573,6 @@ TEST(ISAM2, constrained_ordering)
   EXPECT(isam_check(fullgraph, fullinit, isam, *this, result_));
 
   // Check gradient at each node
-  typedef ISAM2::sharedClique sharedClique;
   for (const auto& [key, clique]: isam.nodes()) {
     // Compute expected gradient
     GaussianFactorGraph jfg;

tests/testGaussianJunctionTreeB.cpp

@@ -65,9 +65,7 @@ using symbol_shorthand::L;
  */
 TEST( GaussianJunctionTreeB, constructor2 ) {
   // create a graph
-  NonlinearFactorGraph nlfg;
+  const auto [nlfg, values] = createNonlinearSmoother(7);
-  Values values;
-  std::tie(nlfg, values) = createNonlinearSmoother(7);
   SymbolicFactorGraph::shared_ptr symbolic = nlfg.symbolic();
 
   // linearize
@@ -125,43 +123,35 @@ TEST( GaussianJunctionTreeB, constructor2 ) {
 }
 
 ///* ************************************************************************* */
-//TEST( GaussianJunctionTreeB, optimizeMultiFrontal )
+TEST(GaussianJunctionTreeB, OptimizeMultiFrontal) {
-//{
+  // create a graph
-//  // create a graph
+  const auto fg = createSmoother(7);
-//  GaussianFactorGraph fg;
+
-//  Ordering ordering;
+  // optimize the graph
-//  std::tie(fg,ordering) = createSmoother(7);
+  const VectorValues actual = fg.optimize(&EliminateQR);
-//
+
-//  // optimize the graph
+  // verify
-//  GaussianJunctionTree tree(fg);
+  VectorValues expected;
-//  VectorValues actual = tree.optimize(&EliminateQR);
+  const Vector v = Vector2(0., 0.);
-//
+  for (int i = 1; i <= 7; i++) expected.emplace(X(i), v);
-//  // verify
+  EXPECT(assert_equal(expected, actual));
-//  VectorValues expected(vector<size_t>(7,2)); // expected solution
+}
-//  Vector v = Vector2(0., 0.);
+
-//  for (int i=1; i<=7; i++)
+/* ************************************************************************* */
-//    expected[ordering[X(i)]] = v;
+TEST(GaussianJunctionTreeB, optimizeMultiFrontal2) {
-//  EXPECT(assert_equal(expected,actual));
+  // create a graph
-//}
+  const auto nlfg = createNonlinearFactorGraph();
-//
+  const auto noisy = createNoisyValues();
-///* ************************************************************************* */
+  const auto fg = *nlfg.linearize(noisy);
-//TEST( GaussianJunctionTreeB, optimizeMultiFrontal2)
+
-//{
+  // optimize the graph
-//  // create a graph
+  VectorValues actual = fg.optimize(&EliminateQR);
-//  example::Graph nlfg = createNonlinearFactorGraph();
+
-//  Values noisy = createNoisyValues();
+  // verify
-//  Ordering ordering; ordering += X(1),X(2),L(1);
+  VectorValues expected = createCorrectDelta();  // expected solution
-//  GaussianFactorGraph fg = *nlfg.linearize(noisy, ordering);
+  EXPECT(assert_equal(expected, actual));
-//
+}
-//  // optimize the graph
+
-//  GaussianJunctionTree tree(fg);
-//  VectorValues actual = tree.optimize(&EliminateQR);
-//
-//  // verify
-//  VectorValues expected = createCorrectDelta(ordering); // expected solution
-//  EXPECT(assert_equal(expected,actual));
-//}
-//
 ///* ************************************************************************* */
 //TEST(GaussianJunctionTreeB, slamlike) {
 //  Values init;

tests/testGncOptimizer.cpp

@@ -737,9 +737,7 @@ TEST(GncOptimizer, setInlierCostThresholds) {
 TEST(GncOptimizer, optimizeSmallPoseGraph) {
   /// load small pose graph
   const string filename = findExampleDataFile("w100.graph");
-  NonlinearFactorGraph::shared_ptr graph;
+  const auto [graph, initial] = load2D(filename);
-  Values::shared_ptr initial;
-  std::tie(graph, initial) = load2D(filename);
   // Add a Gaussian prior on first poses
   Pose2 priorMean(0.0, 0.0, 0.0);  // prior at origin
   SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(

tests/testGradientDescentOptimizer.cpp

@@ -63,16 +63,12 @@ std::tuple<NonlinearFactorGraph, Values> generateProblem() {
   Pose2 x5(2.1, 2.1, -M_PI_2);
   initialEstimate.insert(5, x5);
 
-  return std::tie(graph, initialEstimate);
+  return {graph, initialEstimate};
 }
 
 /* ************************************************************************* */
 TEST(NonlinearConjugateGradientOptimizer, Optimize) {
-
+const auto [graph, initialEstimate] = generateProblem();
-  NonlinearFactorGraph graph;
-  Values initialEstimate;
-
-  std::tie(graph, initialEstimate) = generateProblem();
 //  cout << "initial error = " << graph.error(initialEstimate) << endl;
 
   NonlinearOptimizerParams param;

tests/testIterative.cpp

@@ -59,10 +59,8 @@ TEST( Iterative, conjugateGradientDescent )
   VectorValues expected = fg.optimize();
 
   // get matrices
-  Matrix A;
-  Vector b;
   Vector x0 = Z_6x1;
-  std::tie(A, b) = fg.jacobian();
+  const auto [A, b] = fg.jacobian();
   Vector expectedX = (Vector(6) << -0.1, 0.1, -0.1, -0.1, 0.1, -0.2).finished();
 
   // Do conjugate gradient descent, System version

tests/testSubgraphPreconditioner.cpp

@@ -62,9 +62,7 @@ static double error(const GaussianFactorGraph& fg, const VectorValues& x) {
 /* ************************************************************************* */
 TEST(SubgraphPreconditioner, planarGraph) {
   // Check planar graph construction
-  GaussianFactorGraph A;
+  const auto [A, xtrue] = planarGraph(3);
-  VectorValues xtrue;
-  std::tie(A, xtrue) = planarGraph(3);
   LONGS_EQUAL(13, A.size());
   LONGS_EQUAL(9, xtrue.size());
   DOUBLES_EQUAL(0, error(A, xtrue), 1e-9);  // check zero error for xtrue
@@ -78,13 +76,10 @@ TEST(SubgraphPreconditioner, planarGraph) {
 /* ************************************************************************* */
 TEST(SubgraphPreconditioner, splitOffPlanarTree) {
   // Build a planar graph
-  GaussianFactorGraph A;
+  const auto [A, xtrue] = planarGraph(3);
-  VectorValues xtrue;
-  std::tie(A, xtrue) = planarGraph(3);
 
   // Get the spanning tree and constraints, and check their sizes
-  GaussianFactorGraph T, C;
+  const auto [T, C] = splitOffPlanarTree(3, A);
-  std::tie(T, C) = splitOffPlanarTree(3, A);
   LONGS_EQUAL(9, T.size());
   LONGS_EQUAL(4, C.size());
 
@@ -97,14 +92,11 @@ TEST(SubgraphPreconditioner, splitOffPlanarTree) {
 /* ************************************************************************* */
 TEST(SubgraphPreconditioner, system) {
   // Build a planar graph
-  GaussianFactorGraph Ab;
-  VectorValues xtrue;
   size_t N = 3;
-  std::tie(Ab, xtrue) = planarGraph(N);  // A*x-b
+  const auto [Ab, xtrue] = planarGraph(N);  // A*x-b
 
   // Get the spanning tree and remaining graph
-  GaussianFactorGraph Ab1, Ab2;  // A1*x-b1 and A2*x-b2
+  auto [Ab1, Ab2] = splitOffPlanarTree(N, Ab);
-  std::tie(Ab1, Ab2) = splitOffPlanarTree(N, Ab);
 
   // Eliminate the spanning tree to build a prior
   const Ordering ord = planarOrdering(N);
@@ -120,11 +112,9 @@ TEST(SubgraphPreconditioner, system) {
   Ab2.add(key(1, 1), Z_2x2, Z_2x1);
   Ab2.add(key(1, 2), Z_2x2, Z_2x1);
   Ab2.add(key(1, 3), Z_2x2, Z_2x1);
-  Matrix A, A1, A2;
+  const auto [A, b] = Ab.jacobian(ordering);
-  Vector b, b1, b2;
+  const auto [A1, b1] = Ab1.jacobian(ordering);
-  std::tie(A, b) = Ab.jacobian(ordering);
+  const auto [A2, b2] = Ab2.jacobian(ordering);
-  std::tie(A1, b1) = Ab1.jacobian(ordering);
-  std::tie(A2, b2) = Ab2.jacobian(ordering);
   Matrix R1 = Rc1.matrix(ordering).first;
   Matrix Abar(13 * 2, 9 * 2);
   Abar.topRows(9 * 2) = Matrix::Identity(9 * 2, 9 * 2);
@@ -193,14 +183,11 @@ TEST(SubgraphPreconditioner, system) {
 /* ************************************************************************* */
 TEST(SubgraphPreconditioner, conjugateGradients) {
   // Build a planar graph
-  GaussianFactorGraph Ab;
-  VectorValues xtrue;
   size_t N = 3;
-  std::tie(Ab, xtrue) = planarGraph(N);  // A*x-b
+  const auto [Ab, xtrue] = planarGraph(N);  // A*x-b
 
   // Get the spanning tree
-  GaussianFactorGraph Ab1, Ab2;  // A1*x-b1 and A2*x-b2
+  const auto [Ab1, Ab2] = splitOffPlanarTree(N, Ab);
-  std::tie(Ab1, Ab2) = splitOffPlanarTree(N, Ab);
 
   // Eliminate the spanning tree to build a prior
   GaussianBayesNet Rc1 = *Ab1.eliminateSequential();  // R1*x-c1

tests/testSubgraphSolver.cpp

@@ -55,9 +55,7 @@ TEST( SubgraphSolver, Parameters )
 TEST( SubgraphSolver, splitFactorGraph )
 {
   // Build a planar graph
-  GaussianFactorGraph Ab;
+  const auto [Ab, xtrue] = example::planarGraph(N); // A*x-b
-  VectorValues xtrue;
-  std::tie(Ab, xtrue) = example::planarGraph(N); // A*x-b
 
   SubgraphBuilderParameters params;
   params.augmentationFactor = 0.0;
@@ -65,8 +63,7 @@ TEST( SubgraphSolver, splitFactorGraph )
   auto subgraph = builder(Ab);
   EXPECT_LONGS_EQUAL(9, subgraph.size());
 
-  GaussianFactorGraph Ab1, Ab2;
+  const auto [Ab1, Ab2] = splitFactorGraph(Ab, subgraph);
-  std::tie(Ab1, Ab2) = splitFactorGraph(Ab, subgraph);
   EXPECT_LONGS_EQUAL(9, Ab1.size());
   EXPECT_LONGS_EQUAL(13, Ab2.size());
 }
@@ -75,9 +72,7 @@ TEST( SubgraphSolver, splitFactorGraph )
 TEST( SubgraphSolver, constructor1 )
 {
   // Build a planar graph
-  GaussianFactorGraph Ab;
+  const auto [Ab, xtrue] = example::planarGraph(N); // A*x-b
-  VectorValues xtrue;
-  std::tie(Ab, xtrue) = example::planarGraph(N); // A*x-b
 
   // The first constructor just takes a factor graph (and kParameters)
   // and it will split the graph into A1 and A2, where A1 is a spanning tree
@@ -90,14 +85,11 @@ TEST( SubgraphSolver, constructor1 )
 TEST( SubgraphSolver, constructor2 )
 {
   // Build a planar graph
-  GaussianFactorGraph Ab;
-  VectorValues xtrue;
   size_t N = 3;
-  std::tie(Ab, xtrue) = example::planarGraph(N); // A*x-b
+  const auto [Ab, xtrue] = example::planarGraph(N); // A*x-b
 
-  // Get the spanning tree
+  // Get the spanning tree, A1*x-b1 and A2*x-b2
-  GaussianFactorGraph Ab1, Ab2; // A1*x-b1 and A2*x-b2
+  const auto [Ab1, Ab2] = example::splitOffPlanarTree(N, Ab);
-  std::tie(Ab1, Ab2) = example::splitOffPlanarTree(N, Ab);
 
   // The second constructor takes two factor graphs, so the caller can specify
   // the preconditioner (Ab1) and the constraints that are left out (Ab2)
@@ -110,14 +102,12 @@ TEST( SubgraphSolver, constructor2 )
 TEST( SubgraphSolver, constructor3 )
 {
   // Build a planar graph
-  GaussianFactorGraph Ab;
-  VectorValues xtrue;
   size_t N = 3;
-  std::tie(Ab, xtrue) = example::planarGraph(N); // A*x-b
+  const auto [Ab, xtrue] = example::planarGraph(N); // A*x-b
 
   // Get the spanning tree and corresponding kOrdering
-  GaussianFactorGraph Ab1, Ab2; // A1*x-b1 and A2*x-b2
+  // A1*x-b1 and A2*x-b2
-  std::tie(Ab1, Ab2) = example::splitOffPlanarTree(N, Ab);
+  const auto [Ab1, Ab2] = example::splitOffPlanarTree(N, Ab);
 
   // The caller solves |A1*x-b1|^2 == |R1*x-c1|^2, where R1 is square UT
   auto Rc1 = *Ab1.eliminateSequential();

timing/timeLago.cpp

@@ -33,11 +33,9 @@ int main(int argc, char *argv[]) {
   size_t trials = 1;
 
   // read graph
-  Values::shared_ptr solution;
-  NonlinearFactorGraph::shared_ptr g;
   string inputFile = findExampleDataFile("w10000");
   auto model = noiseModel::Diagonal::Sigmas((Vector(3) << 0.05, 0.05, 5.0 * M_PI / 180.0).finished());
-  std::tie(g, solution) = load2D(inputFile, model);
+  const auto [g, solution] = load2D(inputFile, model);
 
   // add noise to create initial estimate
   Values initial;