diff --git a/internal/ceres/autodiff_test.cc b/internal/ceres/autodiff_test.cc
new file mode 100644
index 0000000..04a77ea
--- /dev/null
+++ b/internal/ceres/autodiff_test.cc
@@ -0,0 +1,667 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2015 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: keir@google.com (Keir Mierle)
+
+#include "ceres/internal/autodiff.h"
+
+#include "gtest/gtest.h"
+#include "ceres/random.h"
+
+namespace ceres {
+namespace internal {
+
+template <typename T> inline
+T &RowMajorAccess(T *base, int rows, int cols, int i, int j) {
+  return base[cols * i + j];
+}
+
+// Do (symmetric) finite differencing using the given function object 'b' of
+// type 'B' and scalar type 'T' with step size 'del'.
+//
+// The type B should have a signature
+//
+//   bool operator()(T const *, T *) const;
+//
+// which maps a vector of parameters to a vector of outputs.
+template <typename B, typename T, int M, int N> inline
+bool SymmetricDiff(const B& b,
+                   const T par[N],
+                   T del,           // step size.
+                   T fun[M],
+                   T jac[M * N]) {  // row-major.
+  if (!b(par, fun)) {
+    return false;
+  }
+
+  // Temporary parameter vector.
+  T tmp_par[N];
+  for (int j = 0; j < N; ++j) {
+    tmp_par[j] = par[j];
+  }
+
+  // For each dimension, we do one forward step and one backward step in
+  // parameter space, and store the output vector vectors in these vectors.
+  T fwd_fun[M];
+  T bwd_fun[M];
+
+  for (int j = 0; j < N; ++j) {
+    // Forward step.
+    tmp_par[j] = par[j] + del;
+    if (!b(tmp_par, fwd_fun)) {
+      return false;
+    }
+
+    // Backward step.
+    tmp_par[j] = par[j] - del;
+    if (!b(tmp_par, bwd_fun)) {
+      return false;
+    }
+
+    // Symmetric differencing:
+    //   f'(a) = (f(a + h) - f(a - h)) / (2 h)
+    for (int i = 0; i < M; ++i) {
+      RowMajorAccess(jac, M, N, i, j) =
+          (fwd_fun[i] - bwd_fun[i]) / (T(2) * del);
+    }
+
+    // Restore our temporary vector.
+    tmp_par[j] = par[j];
+  }
+
+  return true;
+}
+
+template <typename A> inline
+void QuaternionToScaledRotation(A const q[4], A R[3 * 3]) {
+  // Make convenient names for elements of q.
+  A a = q[0];
+  A b = q[1];
+  A c = q[2];
+  A d = q[3];
+  // This is not to eliminate common sub-expression, but to
+  // make the lines shorter so that they fit in 80 columns!
+  A aa = a*a;
+  A ab = a*b;
+  A ac = a*c;
+  A ad = a*d;
+  A bb = b*b;
+  A bc = b*c;
+  A bd = b*d;
+  A cc = c*c;
+  A cd = c*d;
+  A dd = d*d;
+#define R(i, j) RowMajorAccess(R, 3, 3, (i), (j))
+  R(0, 0) =  aa+bb-cc-dd; R(0, 1) = A(2)*(bc-ad); R(0, 2) = A(2)*(ac+bd);  // NOLINT
+  R(1, 0) = A(2)*(ad+bc); R(1, 1) =  aa-bb+cc-dd; R(1, 2) = A(2)*(cd-ab);  // NOLINT
+  R(2, 0) = A(2)*(bd-ac); R(2, 1) = A(2)*(ab+cd); R(2, 2) =  aa-bb-cc+dd;  // NOLINT
+#undef R
+}
+
+// A structure for projecting a 3x4 camera matrix and a
+// homogeneous 3D point, to a 2D inhomogeneous point.
+struct Projective {
+  // Function that takes P and X as separate vectors:
+  //   P, X -> x
+  template <typename A>
+  bool operator()(A const P[12], A const X[4], A x[2]) const {
+    A PX[3];
+    for (int i = 0; i < 3; ++i) {
+      PX[i] = RowMajorAccess(P, 3, 4, i, 0) * X[0] +
+              RowMajorAccess(P, 3, 4, i, 1) * X[1] +
+              RowMajorAccess(P, 3, 4, i, 2) * X[2] +
+              RowMajorAccess(P, 3, 4, i, 3) * X[3];
+    }
+    if (PX[2] != 0.0) {
+      x[0] = PX[0] / PX[2];
+      x[1] = PX[1] / PX[2];
+      return true;
+    }
+    return false;
+  }
+
+  // Version that takes P and X packed in one vector:
+  //
+  //   (P, X) -> x
+  //
+  template <typename A>
+  bool operator()(A const P_X[12 + 4], A x[2]) const {
+    return operator()(P_X + 0, P_X + 12, x);
+  }
+};
+
+// Test projective camera model projector.
+TEST(AutoDiff, ProjectiveCameraModel) {
+  srand(5);
+  double const tol = 1e-10;  // floating-point tolerance.
+  double const del = 1e-4;   // finite-difference step.
+  double const err = 1e-6;   // finite-difference tolerance.
+
+  Projective b;
+
+  // Make random P and X, in a single vector.
+  double PX[12 + 4];
+  for (int i = 0; i < 12 + 4; ++i) {
+    PX[i] = RandDouble();
+  }
+
+  // Handy names for the P and X parts.
+  double *P = PX + 0;
+  double *X = PX + 12;
+
+  // Apply the mapping, to get image point b_x.
+  double b_x[2];
+  b(P, X, b_x);
+
+  // Use finite differencing to estimate the Jacobian.
+  double fd_x[2];
+  double fd_J[2 * (12 + 4)];
+  ASSERT_TRUE((SymmetricDiff<Projective, double, 2, 12 + 4>(b, PX, del,
+                                                            fd_x, fd_J)));
+
+  for (int i = 0; i < 2; ++i) {
+    ASSERT_NEAR(fd_x[i], b_x[i], tol);
+  }
+
+  // Use automatic differentiation to compute the Jacobian.
+  double ad_x1[2];
+  double J_PX[2 * (12 + 4)];
+  {
+    double *parameters[] = { PX };
+    double *jacobians[] = { J_PX };
+    ASSERT_TRUE((AutoDifferentiate<StaticParameterDims<12 + 4>>(
+        b, parameters, 2, ad_x1, jacobians)));
+
+    for (int i = 0; i < 2; ++i) {
+      ASSERT_NEAR(ad_x1[i], b_x[i], tol);
+    }
+  }
+
+  // Use automatic differentiation (again), with two arguments.
+  {
+    double ad_x2[2];
+    double J_P[2 * 12];
+    double J_X[2 * 4];
+    double *parameters[] = { P, X };
+    double *jacobians[] = { J_P, J_X };
+    ASSERT_TRUE((AutoDifferentiate<StaticParameterDims<12, 4>>(
+        b, parameters, 2, ad_x2, jacobians)));
+
+    for (int i = 0; i < 2; ++i) {
+      ASSERT_NEAR(ad_x2[i], b_x[i], tol);
+    }
+
+    // Now compare the jacobians we got.
+    for (int i = 0; i < 2; ++i) {
+      for (int j = 0; j < 12 + 4; ++j) {
+        ASSERT_NEAR(J_PX[(12 + 4) * i + j], fd_J[(12 + 4) * i + j], err);
+      }
+
+      for (int j = 0; j < 12; ++j) {
+        ASSERT_NEAR(J_PX[(12 + 4) * i + j], J_P[12 * i + j], tol);
+      }
+      for (int j = 0; j < 4; ++j) {
+        ASSERT_NEAR(J_PX[(12 + 4) * i + 12 + j], J_X[4 * i + j], tol);
+      }
+    }
+  }
+}
+
+// Object to implement the projection by a calibrated camera.
+struct Metric {
+  // The mapping is
+  //
+  //   q, c, X -> x = dehomg(R(q) (X - c))
+  //
+  // where q is a quaternion and c is the center of projection.
+  //
+  // This function takes three input vectors.
+  template <typename A>
+  bool operator()(A const q[4], A const c[3], A const X[3], A x[2]) const {
+    A R[3 * 3];
+    QuaternionToScaledRotation(q, R);
+
+    // Convert the quaternion mapping all the way to projective matrix.
+    A P[3 * 4];
+
+    // Set P(:, 1:3) = R
+    for (int i = 0; i < 3; ++i) {
+      for (int j = 0; j < 3; ++j) {
+        RowMajorAccess(P, 3, 4, i, j) = RowMajorAccess(R, 3, 3, i, j);
+      }
+    }
+
+    // Set P(:, 4) = - R c
+    for (int i = 0; i < 3; ++i) {
+      RowMajorAccess(P, 3, 4, i, 3) =
+        - (RowMajorAccess(R, 3, 3, i, 0) * c[0] +
+           RowMajorAccess(R, 3, 3, i, 1) * c[1] +
+           RowMajorAccess(R, 3, 3, i, 2) * c[2]);
+    }
+
+    A X1[4] = { X[0], X[1], X[2], A(1) };
+    Projective p;
+    return p(P, X1, x);
+  }
+
+  // A version that takes a single vector.
+  template <typename A>
+  bool operator()(A const q_c_X[4 + 3 + 3], A x[2]) const {
+    return operator()(q_c_X, q_c_X + 4, q_c_X + 4 + 3, x);
+  }
+};
+
+// This test is similar in structure to the previous one.
+TEST(AutoDiff, Metric) {
+  srand(5);
+  double const tol = 1e-10;  // floating-point tolerance.
+  double const del = 1e-4;   // finite-difference step.
+  double const err = 1e-5;   // finite-difference tolerance.
+
+  Metric b;
+
+  // Make random parameter vector.
+  double qcX[4 + 3 + 3];
+  for (int i = 0; i < 4 + 3 + 3; ++i)
+    qcX[i] = RandDouble();
+
+  // Handy names.
+  double *q = qcX;
+  double *c = qcX + 4;
+  double *X = qcX + 4 + 3;
+
+  // Compute projection, b_x.
+  double b_x[2];
+  ASSERT_TRUE(b(q, c, X, b_x));
+
+  // Finite differencing estimate of Jacobian.
+  double fd_x[2];
+  double fd_J[2 * (4 + 3 + 3)];
+  ASSERT_TRUE((SymmetricDiff<Metric, double, 2, 4 + 3 + 3>(b, qcX, del,
+                                                           fd_x, fd_J)));
+
+  for (int i = 0; i < 2; ++i) {
+    ASSERT_NEAR(fd_x[i], b_x[i], tol);
+  }
+
+  // Automatic differentiation.
+  double ad_x[2];
+  double J_q[2 * 4];
+  double J_c[2 * 3];
+  double J_X[2 * 3];
+  double *parameters[] = { q, c, X };
+  double *jacobians[] = { J_q, J_c, J_X };
+  ASSERT_TRUE((AutoDifferentiate<StaticParameterDims<4, 3, 3>>(
+      b, parameters, 2, ad_x, jacobians)));
+
+  for (int i = 0; i < 2; ++i) {
+    ASSERT_NEAR(ad_x[i], b_x[i], tol);
+  }
+
+  // Compare the pieces.
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      ASSERT_NEAR(J_q[4 * i + j], fd_J[(4 + 3 + 3) * i + j], err);
+    }
+    for (int j = 0; j < 3; ++j) {
+      ASSERT_NEAR(J_c[3 * i + j], fd_J[(4 + 3 + 3) * i + j + 4], err);
+    }
+    for (int j = 0; j < 3; ++j) {
+      ASSERT_NEAR(J_X[3 * i + j], fd_J[(4 + 3 + 3) * i + j + 4 + 3], err);
+    }
+  }
+}
+
+struct VaryingResidualFunctor {
+  template <typename T>
+  bool operator()(const T x[2], T* y) const {
+    for (int i = 0; i < num_residuals; ++i) {
+      y[i] = T(i) * x[0] * x[1] * x[1];
+    }
+    return true;
+  }
+
+  int num_residuals;
+};
+
+TEST(AutoDiff, VaryingNumberOfResidualsForOneCostFunctorType) {
+  double x[2] = { 1.0, 5.5 };
+  double *parameters[] = { x };
+  const int kMaxResiduals = 10;
+  double J_x[2 * kMaxResiduals];
+  double residuals[kMaxResiduals];
+  double *jacobians[] = { J_x };
+
+  // Use a single functor, but tweak it to produce different numbers of
+  // residuals.
+  VaryingResidualFunctor functor;
+
+  for (int num_residuals = 1; num_residuals < kMaxResiduals; ++num_residuals) {
+    // Tweak the number of residuals to produce.
+    functor.num_residuals = num_residuals;
+
+    // Run autodiff with the new number of residuals.
+    ASSERT_TRUE((AutoDifferentiate<StaticParameterDims<2>>(
+        functor, parameters, num_residuals, residuals, jacobians)));
+
+    const double kTolerance = 1e-14;
+    for (int i = 0; i < num_residuals; ++i) {
+      EXPECT_NEAR(J_x[2 * i + 0], i * x[1] * x[1], kTolerance) << "i: " << i;
+      EXPECT_NEAR(J_x[2 * i + 1], 2 * i * x[0] * x[1], kTolerance)
+          << "i: " << i;
+    }
+  }
+}
+
+struct Residual1Param {
+  template <typename T>
+  bool operator()(const T* x0, T* y) const {
+    y[0] = *x0;
+    return true;
+  }
+};
+
+struct Residual2Param {
+  template <typename T>
+  bool operator()(const T* x0, const T* x1, T* y) const {
+    y[0] = *x0 + pow(*x1, 2);
+    return true;
+  }
+};
+
+struct Residual3Param {
+  template <typename T>
+  bool operator()(const T* x0, const T* x1, const T* x2, T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3);
+    return true;
+  }
+};
+
+struct Residual4Param {
+  template <typename T>
+  bool operator()(const T* x0,
+                  const T* x1,
+                  const T* x2,
+                  const T* x3,
+                  T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4);
+    return true;
+  }
+};
+
+struct Residual5Param {
+  template <typename T>
+  bool operator()(const T* x0,
+                  const T* x1,
+                  const T* x2,
+                  const T* x3,
+                  const T* x4,
+                  T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5);
+    return true;
+  }
+};
+
+struct Residual6Param {
+  template <typename T>
+  bool operator()(const T* x0,
+                  const T* x1,
+                  const T* x2,
+                  const T* x3,
+                  const T* x4,
+                  const T* x5,
+                  T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
+        pow(*x5, 6);
+    return true;
+  }
+};
+
+struct Residual7Param {
+  template <typename T>
+  bool operator()(const T* x0,
+                  const T* x1,
+                  const T* x2,
+                  const T* x3,
+                  const T* x4,
+                  const T* x5,
+                  const T* x6,
+                  T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
+        pow(*x5, 6) + pow(*x6, 7);
+    return true;
+  }
+};
+
+struct Residual8Param {
+  template <typename T>
+  bool operator()(const T* x0,
+                  const T* x1,
+                  const T* x2,
+                  const T* x3,
+                  const T* x4,
+                  const T* x5,
+                  const T* x6,
+                  const T* x7,
+                  T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
+        pow(*x5, 6) + pow(*x6, 7) + pow(*x7, 8);
+    return true;
+  }
+};
+
+struct Residual9Param {
+  template <typename T>
+  bool operator()(const T* x0,
+                  const T* x1,
+                  const T* x2,
+                  const T* x3,
+                  const T* x4,
+                  const T* x5,
+                  const T* x6,
+                  const T* x7,
+                  const T* x8,
+                  T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
+        pow(*x5, 6) + pow(*x6, 7) + pow(*x7, 8) + pow(*x8, 9);
+    return true;
+  }
+};
+
+struct Residual10Param {
+  template <typename T>
+  bool operator()(const T* x0,
+                  const T* x1,
+                  const T* x2,
+                  const T* x3,
+                  const T* x4,
+                  const T* x5,
+                  const T* x6,
+                  const T* x7,
+                  const T* x8,
+                  const T* x9,
+                  T* y) const {
+    y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
+        pow(*x5, 6) + pow(*x6, 7) + pow(*x7, 8) + pow(*x8, 9) + pow(*x9, 10);
+    return true;
+  }
+};
+
+TEST(AutoDiff, VariadicAutoDiff) {
+  double x[10];
+  double residual = 0;
+  double* parameters[10];
+  double jacobian_values[10];
+  double* jacobians[10];
+
+  for (int i = 0; i < 10; ++i) {
+    x[i] = 2.0;
+    parameters[i] = x + i;
+    jacobians[i] = jacobian_values + i;
+  }
+
+  {
+    Residual1Param functor;
+    int num_variables = 1;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual2Param functor;
+    int num_variables = 2;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1, 1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual3Param functor;
+    int num_variables = 3;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1, 1, 1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual4Param functor;
+    int num_variables = 4;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1, 1, 1, 1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual5Param functor;
+    int num_variables = 5;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1, 1, 1, 1, 1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual6Param functor;
+    int num_variables = 6;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1, 1, 1, 1, 1, 1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual7Param functor;
+    int num_variables = 7;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1, 1, 1, 1, 1, 1, 1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual8Param functor;
+    int num_variables = 8;
+    EXPECT_TRUE((AutoDifferentiate<StaticParameterDims<1, 1, 1, 1, 1, 1, 1, 1>>(
+        functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual9Param functor;
+    int num_variables = 9;
+    EXPECT_TRUE(
+        (AutoDifferentiate<StaticParameterDims<1, 1, 1, 1, 1, 1, 1, 1, 1>>(
+            functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+
+  {
+    Residual10Param functor;
+    int num_variables = 10;
+    EXPECT_TRUE(
+        (AutoDifferentiate<StaticParameterDims<1, 1, 1, 1, 1, 1, 1, 1, 1, 1>>(
+            functor, parameters, 1, &residual, jacobians)));
+    EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
+    for (int i = 0; i < num_variables; ++i) {
+      EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
+    }
+  }
+}
+
+// This is fragile test that triggers the alignment bug on
+// i686-apple-darwin10-llvm-g++-4.2 (GCC) 4.2.1. It is quite possible,
+// that other combinations of operating system + compiler will
+// re-arrange the operations in this test.
+//
+// But this is the best (and only) way we know of to trigger this
+// problem for now. A more robust solution that guarantees the
+// alignment of Eigen types used for automatic differentiation would
+// be nice.
+TEST(AutoDiff, AlignedAllocationTest) {
+  // This int is needed to allocate 16 bits on the stack, so that the
+  // next allocation is not aligned by default.
+  char y = 0;
+
+  // This is needed to prevent the compiler from optimizing y out of
+  // this function.
+  y += 1;
+
+  typedef Jet<double, 2> JetT;
+  FixedArray<JetT, (256 * 7) / sizeof(JetT)> x(3);
+
+  // Need this to makes sure that x does not get optimized out.
+  x[0] = x[0] + JetT(1.0);
+}
+
+}  // namespace internal
+}  // namespace ceres