diff --git a/include/ceres/dynamic_autodiff_cost_function.h b/include/ceres/dynamic_autodiff_cost_function.h
index 38bdb0a..5d8f188 100644
--- a/include/ceres/dynamic_autodiff_cost_function.h
+++ b/include/ceres/dynamic_autodiff_cost_function.h
@@ -126,17 +126,28 @@
     vector<Jet<double, Stride>* > jet_parameters(num_parameter_blocks,
         static_cast<Jet<double, Stride>* >(NULL));
     int num_active_parameters = 0;
-    int start_derivative_section = -1;
-    for (int i = 0, parameter_cursor = 0; i < num_parameter_blocks; ++i) {
+
+    // To handle constant parameters between non-constant parameter blocks, the
+    // start position --- a raw parameter index --- of each contiguous block of
+    // non-constant parameters is recorded in start_derivative_section.
+    vector<int> start_derivative_section;
+    bool in_derivative_section = false;
+    int parameter_cursor = 0;
+
+    // Discover the derivative sections and set the parameter values.
+    for (int i = 0; i < num_parameter_blocks; ++i) {
       jet_parameters[i] = &input_jets[parameter_cursor];
 
       const int parameter_block_size = parameter_block_sizes()[i];
       if (jacobians[i] != NULL) {
-        start_derivative_section =
-            (start_derivative_section == -1)
-            ? parameter_cursor
-            : start_derivative_section;
+        if (!in_derivative_section) {
+          start_derivative_section.push_back(parameter_cursor);
+          in_derivative_section = true;
+        }
+
         num_active_parameters += parameter_block_size;
+      } else {
+        in_derivative_section = false;
       }
 
       for (int j = 0; j < parameter_block_size; ++j, parameter_cursor++) {
@@ -144,29 +155,54 @@
       }
     }
 
+    // When `num_active_parameters % Stride != 0` then it can be the case
+    // that `active_parameter_count < Stride` while parameter_cursor is less
+    // than the total number of parameters and with no remaining non-constant
+    // parameter blocks. Pushing parameter_cursor (the total number of
+    // parameters) as a final entry to start_derivative_section is required
+    // because if a constant parameter block is encountered after the
+    // last non-constant block then current_derivative_section is incremented
+    // and would otherwise index an invalid position in
+    // start_derivative_section. Setting the final element to the total number
+    // of parameters means that this can only happen at most once in the loop
+    // below.
+    start_derivative_section.push_back(parameter_cursor);
+
     // Evaluate all of the strides. Each stride is a chunk of the derivative to
     // evaluate, typically some size proportional to the size of the SIMD
     // registers of the CPU.
     int num_strides = static_cast<int>(ceil(num_active_parameters /
                                             static_cast<float>(Stride)));
 
+    int current_derivative_section = 0;
+    int current_derivative_section_cursor = 0;
+
     for (int pass = 0; pass < num_strides; ++pass) {
       // Set most of the jet components to zero, except for
       // non-constant #Stride parameters.
+      const int initial_derivative_section = current_derivative_section;
+      const int initial_derivative_section_cursor =
+        current_derivative_section_cursor;
+
       int active_parameter_count = 0;
-      int end_derivative_section = start_derivative_section;
-      for (int i = 0, parameter_cursor = 0; i < num_parameter_blocks; ++i) {
+      parameter_cursor = 0;
+
+      for (int i = 0; i < num_parameter_blocks; ++i) {
         for (int j = 0; j < parameter_block_sizes()[i];
              ++j, parameter_cursor++) {
           input_jets[parameter_cursor].v.setZero();
-          if (parameter_cursor >= start_derivative_section &&
-              active_parameter_count < Stride) {
+          if (active_parameter_count < Stride &&
+              parameter_cursor >= (
+                start_derivative_section[current_derivative_section] +
+                current_derivative_section_cursor)) {
             if (jacobians[i] != NULL) {
-              input_jets[parameter_cursor]
-                  .v[parameter_cursor - start_derivative_section] = 1.0;
+              input_jets[parameter_cursor].v[active_parameter_count] = 1.0;
               ++active_parameter_count;
+              ++current_derivative_section_cursor;
+            } else {
+              ++current_derivative_section;
+              current_derivative_section_cursor = 0;
             }
-            ++end_derivative_section;
           }
         }
       }
@@ -177,18 +213,27 @@
 
       // Copy the pieces of the jacobians into their final place.
       active_parameter_count = 0;
+
+      current_derivative_section = initial_derivative_section;
+      current_derivative_section_cursor = initial_derivative_section_cursor;
+
       for (int i = 0, parameter_cursor = 0; i < num_parameter_blocks; ++i) {
         for (int j = 0; j < parameter_block_sizes()[i];
              ++j, parameter_cursor++) {
-          if (parameter_cursor >= start_derivative_section &&
-              active_parameter_count < Stride) {
+          if (active_parameter_count < Stride &&
+              parameter_cursor >= (
+                start_derivative_section[current_derivative_section] +
+                current_derivative_section_cursor)) {
             if (jacobians[i] != NULL) {
               for (int k = 0; k < num_residuals(); ++k) {
                 jacobians[i][k * parameter_block_sizes()[i] + j] =
-                    output_jets[k].v[parameter_cursor -
-                                     start_derivative_section];
+                    output_jets[k].v[active_parameter_count];
               }
               ++active_parameter_count;
+              ++current_derivative_section_cursor;
+            } else {
+              ++current_derivative_section;
+              current_derivative_section_cursor = 0;
             }
           }
         }
@@ -201,8 +246,6 @@
           residuals[k] = output_jets[k].a;
         }
       }
-
-      start_derivative_section = end_derivative_section;
     }
     return true;
   }
diff --git a/internal/ceres/dynamic_autodiff_cost_function_test.cc b/internal/ceres/dynamic_autodiff_cost_function_test.cc
index b62b56a..a42a374 100644
--- a/internal/ceres/dynamic_autodiff_cost_function_test.cc
+++ b/internal/ceres/dynamic_autodiff_cost_function_test.cc
@@ -31,6 +31,7 @@
 //         sameeragarwal@google.com (Sameer Agarwal)
 
 #include "ceres/dynamic_autodiff_cost_function.h"
+#include "ceres/internal/scoped_ptr.h"
 
 #include <cstddef>
 
@@ -270,5 +271,504 @@
   }
 }
 
+// Takes 3 parameter blocks:
+//     parameters[0] (x) is size 1.
+//     parameters[1] (y) is size 2.
+//     parameters[2] (z) is size 3.
+// Emits 7 residuals:
+//     A: x[0] (= sum_x)
+//     B: y[0] + 2.0 * y[1] (= sum_y)
+//     C: z[0] + 3.0 * z[1] + 6.0 * z[2] (= sum_z)
+//     D: sum_x * sum_y
+//     E: sum_y * sum_z
+//     F: sum_x * sum_z
+//     G: sum_x * sum_y * sum_z
+class MyThreeParameterCostFunctor {
+ public:
+  template <typename T>
+  bool operator()(T const* const* parameters, T* residuals) const {
+    const T* x = parameters[0];
+    const T* y = parameters[1];
+    const T* z = parameters[2];
+
+    T sum_x = x[0];
+    T sum_y = y[0] + 2.0 * y[1];
+    T sum_z = z[0] + 3.0 * z[1] + 6.0 * z[2];
+
+    residuals[0] = sum_x;
+    residuals[1] = sum_y;
+    residuals[2] = sum_z;
+    residuals[3] = sum_x * sum_y;
+    residuals[4] = sum_y * sum_z;
+    residuals[5] = sum_x * sum_z;
+    residuals[6] = sum_x * sum_y * sum_z;
+    return true;
+  }
+};
+
+class ThreeParameterCostFunctorTest : public ::testing::Test {
+ protected:
+  virtual void SetUp() {
+    // Prepare the parameters.
+    x_.resize(1);
+    x_[0] = 0.0;
+
+    y_.resize(2);
+    y_[0] = 1.0;
+    y_[1] = 3.0;
+
+    z_.resize(3);
+    z_[0] = 2.0;
+    z_[1] = 4.0;
+    z_[2] = 6.0;
+
+    parameter_blocks_.resize(3);
+    parameter_blocks_[0] = &x_[0];
+    parameter_blocks_[1] = &y_[0];
+    parameter_blocks_[2] = &z_[0];
+
+    // Prepare the cost function.
+    typedef DynamicAutoDiffCostFunction<MyThreeParameterCostFunctor, 3>
+      DynamicMyThreeParameterCostFunction;
+    DynamicMyThreeParameterCostFunction * cost_function =
+      new DynamicMyThreeParameterCostFunction(
+        new MyThreeParameterCostFunctor());
+    cost_function->AddParameterBlock(1);
+    cost_function->AddParameterBlock(2);
+    cost_function->AddParameterBlock(3);
+    cost_function->SetNumResiduals(7);
+
+    cost_function_.reset(cost_function);
+
+    // Setup jacobian data.
+    jacobian_vect_.resize(3);
+    jacobian_vect_[0].resize(7 * x_.size(), -100000);
+    jacobian_vect_[1].resize(7 * y_.size(), -100000);
+    jacobian_vect_[2].resize(7 * z_.size(), -100000);
+
+    // Prepare the expected residuals.
+    const double sum_x = x_[0];
+    const double sum_y = y_[0] + 2.0 * y_[1];
+    const double sum_z = z_[0] + 3.0 * z_[1] + 6.0 * z_[2];
+
+    expected_residuals_.resize(7);
+    expected_residuals_[0] = sum_x;
+    expected_residuals_[1] = sum_y;
+    expected_residuals_[2] = sum_z;
+    expected_residuals_[3] = sum_x * sum_y;
+    expected_residuals_[4] = sum_y * sum_z;
+    expected_residuals_[5] = sum_x * sum_z;
+    expected_residuals_[6] = sum_x * sum_y * sum_z;
+
+    // Prepare the expected jacobian entries.
+    expected_jacobian_x_.resize(7);
+    expected_jacobian_x_[0] = 1.0;
+    expected_jacobian_x_[1] = 0.0;
+    expected_jacobian_x_[2] = 0.0;
+    expected_jacobian_x_[3] = sum_y;
+    expected_jacobian_x_[4] = 0.0;
+    expected_jacobian_x_[5] = sum_z;
+    expected_jacobian_x_[6] = sum_y * sum_z;
+
+    expected_jacobian_y_.resize(14);
+    expected_jacobian_y_[0] = 0.0;
+    expected_jacobian_y_[1] = 0.0;
+    expected_jacobian_y_[2] = 1.0;
+    expected_jacobian_y_[3] = 2.0;
+    expected_jacobian_y_[4] = 0.0;
+    expected_jacobian_y_[5] = 0.0;
+    expected_jacobian_y_[6] = sum_x;
+    expected_jacobian_y_[7] = 2.0 * sum_x;
+    expected_jacobian_y_[8] = sum_z;
+    expected_jacobian_y_[9] = 2.0 * sum_z;
+    expected_jacobian_y_[10] = 0.0;
+    expected_jacobian_y_[11] = 0.0;
+    expected_jacobian_y_[12] = sum_x * sum_z;
+    expected_jacobian_y_[13] = 2.0 * sum_x * sum_z;
+
+    expected_jacobian_z_.resize(21);
+    expected_jacobian_z_[0] = 0.0;
+    expected_jacobian_z_[1] = 0.0;
+    expected_jacobian_z_[2] = 0.0;
+    expected_jacobian_z_[3] = 0.0;
+    expected_jacobian_z_[4] = 0.0;
+    expected_jacobian_z_[5] = 0.0;
+    expected_jacobian_z_[6] = 1.0;
+    expected_jacobian_z_[7] = 3.0;
+    expected_jacobian_z_[8] = 6.0;
+    expected_jacobian_z_[9] = 0.0;
+    expected_jacobian_z_[10] = 0.0;
+    expected_jacobian_z_[11] = 0.0;
+    expected_jacobian_z_[12] = sum_y;
+    expected_jacobian_z_[13] = 3.0 * sum_y;
+    expected_jacobian_z_[14] = 6.0 * sum_y;
+    expected_jacobian_z_[15] = sum_x;
+    expected_jacobian_z_[16] = 3.0 * sum_x;
+    expected_jacobian_z_[17] = 6.0 * sum_x;
+    expected_jacobian_z_[18] = sum_x * sum_y;
+    expected_jacobian_z_[19] = 3.0 * sum_x * sum_y;
+    expected_jacobian_z_[20] = 6.0 * sum_x * sum_y;
+  }
+
+ protected:
+  vector<double> x_;
+  vector<double> y_;
+  vector<double> z_;
+
+  vector<double*> parameter_blocks_;
+
+  scoped_ptr<CostFunction> cost_function_;
+
+  vector<vector<double> > jacobian_vect_;
+
+  vector<double> expected_residuals_;
+
+  vector<double> expected_jacobian_x_;
+  vector<double> expected_jacobian_y_;
+  vector<double> expected_jacobian_z_;
+};
+
+TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterResiduals) {
+  vector<double> residuals(7, -100000);
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       NULL));
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+}
+
+TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterJacobian) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect_[0].data());
+  jacobian.push_back(jacobian_vect_[1].data());
+  jacobian.push_back(jacobian_vect_[2].data());
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_jacobian_x_[i], jacobian[0][i]);
+  }
+
+  for (int i = 0; i < 14; ++i) {
+    EXPECT_EQ(expected_jacobian_y_[i], jacobian[1][i]);
+  }
+
+  for (int i = 0; i < 21; ++i) {
+    EXPECT_EQ(expected_jacobian_z_[i], jacobian[2][i]);
+  }
+}
+
+TEST_F(ThreeParameterCostFunctorTest,
+       ThreeParameterJacobianWithFirstAndLastParameterBlockConstant) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect_[1].data());
+  jacobian.push_back(NULL);
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 14; ++i) {
+    EXPECT_EQ(expected_jacobian_y_[i], jacobian[1][i]);
+  }
+}
+
+TEST_F(ThreeParameterCostFunctorTest,
+       ThreeParameterJacobianWithSecondParameterBlockConstant) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect_[0].data());
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect_[2].data());
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_jacobian_x_[i], jacobian[0][i]);
+  }
+
+  for (int i = 0; i < 21; ++i) {
+    EXPECT_EQ(expected_jacobian_z_[i], jacobian[2][i]);
+  }
+}
+
+// Takes 6 parameter blocks all of size 1:
+//     x0, y0, y1, z0, z1, z2
+// Same 7 residuals as MyThreeParameterCostFunctor.
+// Naming convention for tests is (V)ariable and (C)onstant.
+class MySixParameterCostFunctor {
+ public:
+  template <typename T>
+  bool operator()(T const* const* parameters, T* residuals) const {
+    const T* x0 = parameters[0];
+    const T* y0 = parameters[1];
+    const T* y1 = parameters[2];
+    const T* z0 = parameters[3];
+    const T* z1 = parameters[4];
+    const T* z2 = parameters[5];
+
+    T sum_x = x0[0];
+    T sum_y = y0[0] + 2.0 * y1[0];
+    T sum_z = z0[0] + 3.0 * z1[0] + 6.0 * z2[0];
+
+    residuals[0] = sum_x;
+    residuals[1] = sum_y;
+    residuals[2] = sum_z;
+    residuals[3] = sum_x * sum_y;
+    residuals[4] = sum_y * sum_z;
+    residuals[5] = sum_x * sum_z;
+    residuals[6] = sum_x * sum_y * sum_z;
+    return true;
+  }
+};
+
+class SixParameterCostFunctorTest : public ::testing::Test {
+ protected:
+  virtual void SetUp() {
+    // Prepare the parameters.
+    x0_ = 0.0;
+    y0_ = 1.0;
+    y1_ = 3.0;
+    z0_ = 2.0;
+    z1_ = 4.0;
+    z2_ = 6.0;
+
+    parameter_blocks_.resize(6);
+    parameter_blocks_[0] = &x0_;
+    parameter_blocks_[1] = &y0_;
+    parameter_blocks_[2] = &y1_;
+    parameter_blocks_[3] = &z0_;
+    parameter_blocks_[4] = &z1_;
+    parameter_blocks_[5] = &z2_;
+
+    // Prepare the cost function.
+    typedef DynamicAutoDiffCostFunction<MySixParameterCostFunctor, 3>
+      DynamicMySixParameterCostFunction;
+    DynamicMySixParameterCostFunction * cost_function =
+      new DynamicMySixParameterCostFunction(
+        new MySixParameterCostFunctor());
+    for (int i = 0; i < 6; ++i) {
+      cost_function->AddParameterBlock(1);
+    }
+    cost_function->SetNumResiduals(7);
+
+    cost_function_.reset(cost_function);
+
+    // Setup jacobian data.
+    jacobian_vect_.resize(6);
+    for (int i = 0; i < 6; ++i) {
+      jacobian_vect_[i].resize(7, -100000);
+    }
+
+    // Prepare the expected residuals.
+    const double sum_x = x0_;
+    const double sum_y = y0_ + 2.0 * y1_;
+    const double sum_z = z0_ + 3.0 * z1_ + 6.0 * z2_;
+
+    expected_residuals_.resize(7);
+    expected_residuals_[0] = sum_x;
+    expected_residuals_[1] = sum_y;
+    expected_residuals_[2] = sum_z;
+    expected_residuals_[3] = sum_x * sum_y;
+    expected_residuals_[4] = sum_y * sum_z;
+    expected_residuals_[5] = sum_x * sum_z;
+    expected_residuals_[6] = sum_x * sum_y * sum_z;
+
+    // Prepare the expected jacobian entries.
+    expected_jacobians_.resize(6);
+    expected_jacobians_[0].resize(7);
+    expected_jacobians_[0][0] = 1.0;
+    expected_jacobians_[0][1] = 0.0;
+    expected_jacobians_[0][2] = 0.0;
+    expected_jacobians_[0][3] = sum_y;
+    expected_jacobians_[0][4] = 0.0;
+    expected_jacobians_[0][5] = sum_z;
+    expected_jacobians_[0][6] = sum_y * sum_z;
+
+    expected_jacobians_[1].resize(7);
+    expected_jacobians_[1][0] = 0.0;
+    expected_jacobians_[1][1] = 1.0;
+    expected_jacobians_[1][2] = 0.0;
+    expected_jacobians_[1][3] = sum_x;
+    expected_jacobians_[1][4] = sum_z;
+    expected_jacobians_[1][5] = 0.0;
+    expected_jacobians_[1][6] = sum_x * sum_z;
+
+    expected_jacobians_[2].resize(7);
+    expected_jacobians_[2][0] = 0.0;
+    expected_jacobians_[2][1] = 2.0;
+    expected_jacobians_[2][2] = 0.0;
+    expected_jacobians_[2][3] = 2.0 * sum_x;
+    expected_jacobians_[2][4] = 2.0 * sum_z;
+    expected_jacobians_[2][5] = 0.0;
+    expected_jacobians_[2][6] = 2.0 * sum_x * sum_z;
+
+    expected_jacobians_[3].resize(7);
+    expected_jacobians_[3][0] = 0.0;
+    expected_jacobians_[3][1] = 0.0;
+    expected_jacobians_[3][2] = 1.0;
+    expected_jacobians_[3][3] = 0.0;
+    expected_jacobians_[3][4] = sum_y;
+    expected_jacobians_[3][5] = sum_x;
+    expected_jacobians_[3][6] = sum_x * sum_y;
+
+    expected_jacobians_[4].resize(7);
+    expected_jacobians_[4][0] = 0.0;
+    expected_jacobians_[4][1] = 0.0;
+    expected_jacobians_[4][2] = 3.0;
+    expected_jacobians_[4][3] = 0.0;
+    expected_jacobians_[4][4] = 3.0 * sum_y;
+    expected_jacobians_[4][5] = 3.0 * sum_x;
+    expected_jacobians_[4][6] = 3.0 * sum_x * sum_y;
+
+    expected_jacobians_[5].resize(7);
+    expected_jacobians_[5][0] = 0.0;
+    expected_jacobians_[5][1] = 0.0;
+    expected_jacobians_[5][2] = 6.0;
+    expected_jacobians_[5][3] = 0.0;
+    expected_jacobians_[5][4] = 6.0 * sum_y;
+    expected_jacobians_[5][5] = 6.0 * sum_x;
+    expected_jacobians_[5][6] = 6.0 * sum_x * sum_y;
+  }
+
+ protected:
+  double x0_;
+  double y0_;
+  double y1_;
+  double z0_;
+  double z1_;
+  double z2_;
+
+  vector<double*> parameter_blocks_;
+
+  scoped_ptr<CostFunction> cost_function_;
+
+  vector<vector<double> > jacobian_vect_;
+
+  vector<double> expected_residuals_;
+  vector<vector<double> > expected_jacobians_;
+};
+
+TEST_F(SixParameterCostFunctorTest, TestSixParameterResiduals) {
+  vector<double> residuals(7, -100000);
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       NULL));
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+}
+
+TEST_F(SixParameterCostFunctorTest, TestSixParameterJacobian) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect_[0].data());
+  jacobian.push_back(jacobian_vect_[1].data());
+  jacobian.push_back(jacobian_vect_[2].data());
+  jacobian.push_back(jacobian_vect_[3].data());
+  jacobian.push_back(jacobian_vect_[4].data());
+  jacobian.push_back(jacobian_vect_[5].data());
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 6; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      EXPECT_EQ(expected_jacobians_[i][j], jacobian[i][j]);
+    }
+  }
+}
+
+TEST_F(SixParameterCostFunctorTest, TestSixParameterJacobianVVCVVC) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect_[0].data());
+  jacobian.push_back(jacobian_vect_[1].data());
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect_[3].data());
+  jacobian.push_back(jacobian_vect_[4].data());
+  jacobian.push_back(NULL);
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 6; ++i) {
+    // Skip the constant variables.
+    if (i == 2 || i == 5) {
+      continue;
+    }
+
+    for (int j = 0; j < 7; ++j) {
+      EXPECT_EQ(expected_jacobians_[i][j], jacobian[i][j]);
+    }
+  }
+}
+
+TEST_F(SixParameterCostFunctorTest, TestSixParameterJacobianVCCVCV) {
+  vector<double> residuals(7, -100000);
+
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect_[0].data());
+  jacobian.push_back(NULL);
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect_[3].data());
+  jacobian.push_back(NULL);
+  jacobian.push_back(jacobian_vect_[5].data());
+
+  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),
+                                       residuals.data(),
+                                       jacobian.data()));
+
+  for (int i = 0; i < 7; ++i) {
+    EXPECT_EQ(expected_residuals_[i], residuals[i]);
+  }
+
+  for (int i = 0; i < 6; ++i) {
+    // Skip the constant variables.
+    if (i == 1 || i == 2 || i == 4) {
+      continue;
+    }
+
+    for (int j = 0; j < 7; ++j) {
+      EXPECT_EQ(expected_jacobians_[i][j], jacobian[i][j]);
+    }
+  }
+}
+
 }  // namespace internal
 }  // namespace ceres