diff --git a/include/ceres/dynamic_autodiff_cost_function.h b/include/ceres/dynamic_autodiff_cost_function.h
new file mode 100644
index 0000000..0bc18eb
--- /dev/null
+++ b/include/ceres/dynamic_autodiff_cost_function.h
@@ -0,0 +1,189 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// 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: mierle@gmail.com (Keir Mierle)
+//         sameeragarwal@google.com (Sameer Agarwal)
+//         thadh@gmail.com (Thad Hughes)
+//
+// This autodiff implementation differs from the one found in
+// autodiff_cost_function.h by supporting autodiff on cost functions with
+// variable numbers of parameters with variable sizes. With the other
+// implementation, all the sizes (both the number of parameter blocks and the
+// size of each block) must be fixed at compile time.
+//
+// The functor API differs slightly from the API for fixed size autodiff; the
+// expected interface for the cost functors is:
+//
+//   struct MyCostFunctor {
+//     template<typename T>
+//     bool operator()(const* const* T parameters, T* residuals) const {
+//       // Use parameters[i] to access the i'th parameter block.
+//     }
+//   }
+//
+// Since the sizing of the parameters is done at runtime, you must also specify
+// the sizes after creating the dynamic autodiff cost function. For example:
+//
+//   DynamicAutoDiffCostFunction<MyCostFunctor, 3> cost_function(
+//       new MyCostFunctor());
+//   cost_function.AddParameterBlock(5);
+//   cost_function.AddParameterBlock(10);
+//   cost_function.SetNumResiduals(21);
+//
+// Under the hood, the implementation evaluates the cost function multiple
+// times, computing a small set of the derivatives (four by default, controlled
+// by the Stride template parameter) with each pass. There is a tradeoff with
+// the size of the passes; you may want to experiment with the stride.
+
+#ifndef CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
+#define CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
+
+#include <cmath>
+#include <numeric>
+#include <glog/logging.h>
+#include "ceres/cost_function.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/jet.h"
+
+namespace ceres {
+
+template <typename CostFunctor, int Stride = 4>
+class DynamicAutoDiffCostFunction : public CostFunction {
+ public:
+  DynamicAutoDiffCostFunction(CostFunctor* functor)
+    : functor_(functor) {}
+
+  virtual ~DynamicAutoDiffCostFunction() {}
+
+  void AddParameterBlock(int size) {
+    mutable_parameter_block_sizes()->push_back(size);
+  }
+
+  void SetNumResiduals(int num_residuals) {
+    set_num_residuals(num_residuals);
+  }
+
+  virtual bool Evaluate(double const* const* parameters,
+                        double* residuals,
+                        double** jacobians) const {
+    CHECK_GT(num_residuals(), 0)
+        << "You must call DynamicAutoDiffCostFunction::SetNumResiduals() "
+        << "before DynamicAutoDiffCostFunction::Evaluate().";
+
+    if (jacobians == NULL) {
+      return (*functor_)(parameters, residuals);
+    }
+
+    // The difficulty with Jets, as implemented in Ceres, is that they were
+    // originally designed for strictly compile-sized use. At this point, there
+    // is a large body of code that assumes inside a cost functor it is
+    // acceptable to do e.g. T(1.5) and get an appropriately sized jet back.
+    //
+    // Unfortunately, it is impossible to communicate the expected size of a
+    // dynamically sized jet to the static instantiations that existing code
+    // depends on.
+    //
+    // To work around this issue, the solution here is to evaluate the
+    // jacobians in a series of passes, each one computing Stripe *
+    // num_residuals() derivatives. This is done with small, fixed-size jets.
+    const int num_parameter_blocks = parameter_block_sizes().size();
+    const int num_parameters = std::accumulate(parameter_block_sizes().begin(),
+                                               parameter_block_sizes().end(),
+                                               0);
+
+    // Allocate scratch space for the strided evaluation.
+    vector<Jet<double, Stride> > input_jets(num_parameters);
+    vector<Jet<double, Stride> > output_jets(num_residuals());
+
+    // Make the parameter pack that is sent to the functor (reused).
+    vector<Jet<double, Stride>* > jet_parameters(num_parameter_blocks);
+    for (int i = 0, parameter_cursor = 0; i < num_parameter_blocks; ++i) {
+      jet_parameters[i] = &input_jets[parameter_cursor];
+      for (int j = 0; j < parameter_block_sizes()[i]; ++j, parameter_cursor++) {
+        input_jets[parameter_cursor].a = parameters[i][j];
+      }
+    }
+
+    // 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 = int(ceil(num_parameters / float(Stride)));
+    for (int pass = 0; pass < num_strides; ++pass) {
+      const int start_derivative_section = pass * Stride;
+      const int end_derivative_section = std::min((pass + 1) * Stride,
+                                                  num_parameters);
+      // Set most of the jet components to zero, except for the active
+      // parameters, which occur in a contiguos block of size Stride.
+      for (int i = 0, parameter_cursor = 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 &&
+              parameter_cursor < end_derivative_section) {
+            input_jets[parameter_cursor]
+                .v[parameter_cursor - start_derivative_section] = 1.0;
+          }
+        }
+      }
+
+      if (!(*functor_)(&jet_parameters[0], &output_jets[0])) {
+        return false;
+      }
+
+      // Copy the pieces of the jacobians into their final place.
+      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 &&
+              parameter_cursor < end_derivative_section) {
+            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];
+            }
+          }
+        }
+      }
+
+      // Only copy the residuals over once (even though we compute them on
+      // every loop).
+      if (pass == num_strides - 1) {
+        for (int k = 0; k < num_residuals(); ++k) {
+          residuals[k] = output_jets[k].a;
+        }
+      }
+    }
+    return true;
+  }
+
+ private:
+  internal::scoped_ptr<CostFunctor> functor_;
+};
+
+}  // namespace ceres
+
+#endif  // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
diff --git a/internal/ceres/CMakeLists.txt b/internal/ceres/CMakeLists.txt
index f9329e9..b30f0cc 100644
--- a/internal/ceres/CMakeLists.txt
+++ b/internal/ceres/CMakeLists.txt
@@ -225,6 +225,7 @@
   CERES_TEST(corrector)
   CERES_TEST(cost_function_to_functor)
   CERES_TEST(dense_sparse_matrix)
+  CERES_TEST(dynamic_autodiff_cost_function)
   CERES_TEST(evaluator)
   CERES_TEST(gradient_checker)
   CERES_TEST(gradient_checking_cost_function)
diff --git a/internal/ceres/dynamic_autodiff_cost_function_test.cc b/internal/ceres/dynamic_autodiff_cost_function_test.cc
new file mode 100644
index 0000000..5d743a7
--- /dev/null
+++ b/internal/ceres/dynamic_autodiff_cost_function_test.cc
@@ -0,0 +1,166 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// 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: thadh@gmail.com (Thad Hughes)
+//         mierle@gmail.com (Keir Mierle)
+//         sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/dynamic_autodiff_cost_function.h"
+
+#include <cstddef>
+
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+// Takes 2 parameter blocks:
+//     parameters[0] is size 10.
+//     parameters[1] is size 5.
+// Emits 21 residuals:
+//     A: i - parameters[0][i], for i in [0,10)  -- this is 10 residuals
+//     B: parameters[0][i] - i, for i in [0,10)  -- this is another 10.
+//     C: sum(parameters[0][i]^2 - 8*parameters[0][i]) + sum(parameters[1][i])
+class MyCostFunctor {
+ public:
+  template <typename T>
+  bool operator()(T const* const* parameters, T* residuals) const {
+    const T* params0 = parameters[0];
+    int r = 0;
+    for (int i = 0; i < 10; ++i) {
+      residuals[r++] = T(i) - params0[i];
+      residuals[r++] = params0[i] - T(i);
+    }
+
+    T c_residual(0.0);
+    for (int i = 0; i < 10; ++i) {
+      c_residual += pow(params0[i], 2) - T(8) * params0[i];
+    }
+
+    const T* params1 = parameters[1];
+    for (int i = 0; i < 5; ++i) {
+      c_residual += params1[i];
+    }
+    residuals[r++] = c_residual;
+    return true;
+  }
+};
+
+TEST(DynamicAutodiffCostFunctionTest, TestResiduals) {
+  vector<double> param_block_0(10, 0.0);
+  vector<double> param_block_1(5, 0.0);
+  DynamicAutoDiffCostFunction<MyCostFunctor, 3> cost_function(
+      new MyCostFunctor());
+  cost_function.AddParameterBlock(param_block_0.size());
+  cost_function.AddParameterBlock(param_block_1.size());
+  cost_function.SetNumResiduals(21);
+
+  // Test residual computation.
+  vector<double> residuals(21, -100000);
+  vector<double*> parameter_blocks(2);
+  parameter_blocks[0] = &param_block_0[0];
+  parameter_blocks[1] = &param_block_1[0];
+  EXPECT_TRUE(cost_function.Evaluate(&parameter_blocks[0],
+                                     residuals.data(),
+                                     NULL));
+  for (int r = 0; r < 10; ++r) {
+    EXPECT_EQ(1.0 * r, residuals.at(r * 2));
+    EXPECT_EQ(-1.0 * r, residuals.at(r * 2 + 1));
+  }
+  EXPECT_EQ(0, residuals.at(20));
+}
+
+TEST(DynamicAutodiffCostFunctionTest, TestJacobian) {
+  // Test the residual counting.
+  vector<double> param_block_0(10, 0.0);
+  for (int i = 0; i < 10; ++i) {
+    param_block_0[i] = 2 * i;
+  }
+  vector<double> param_block_1(5, 0.0);
+  DynamicAutoDiffCostFunction<MyCostFunctor, 3> cost_function(
+      new MyCostFunctor());
+  cost_function.AddParameterBlock(param_block_0.size());
+  cost_function.AddParameterBlock(param_block_1.size());
+  cost_function.SetNumResiduals(21);
+
+  // Prepare the residuals.
+  vector<double> residuals(21, -100000);
+
+  // Prepare the parameters.
+  vector<double*> parameter_blocks(2);
+  parameter_blocks[0] = &param_block_0[0];
+  parameter_blocks[1] = &param_block_1[0];
+
+  // Prepare the jacobian.
+  vector<vector<double> > jacobian_vect(2);
+  jacobian_vect[0].resize(21 * 10, -100000);
+  jacobian_vect[1].resize(21 * 5, -100000);
+  vector<double*> jacobian;
+  jacobian.push_back(jacobian_vect[0].data());
+  jacobian.push_back(jacobian_vect[1].data());
+
+  // Test jacobian computation.
+  EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),
+                                     residuals.data(),
+                                     jacobian.data()));
+
+  for (int r = 0; r < 10; ++r) {
+    EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
+    EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
+  }
+  EXPECT_EQ(420, residuals.at(20));
+  for (int p = 0; p < 10; ++p) {
+    // Check "A" Jacobian.
+    EXPECT_EQ(-1.0, jacobian_vect[0][2*p * 10 + p]);
+    // Check "B" Jacobian.
+    EXPECT_EQ(+1.0, jacobian_vect[0][(2*p+1) * 10 + p]);
+    jacobian_vect[0][2*p * 10 + p] = 0.0;
+    jacobian_vect[0][(2*p+1) * 10 + p] = 0.0;
+  }
+
+  // Check "C" Jacobian for first parameter block.
+  for (int p = 0; p < 10; ++p) {
+    EXPECT_EQ(4 * p - 8, jacobian_vect[0][20 * 10 + p]);
+    jacobian_vect[0][20 * 10 + p] = 0.0;
+  }
+  for (int i = 0; i < jacobian_vect[0].size(); ++i) {
+    EXPECT_EQ(0.0, jacobian_vect[0][i]);
+  }
+
+  // Check "C" Jacobian for second parameter block.
+  for (int p = 0; p < 5; ++p) {
+    EXPECT_EQ(1.0, jacobian_vect[1][20 * 5 + p]);
+    jacobian_vect[1][20 * 5 + p] = 0.0;
+  }
+  for (int i = 0; i < jacobian_vect[1].size(); ++i) {
+    EXPECT_EQ(0.0, jacobian_vect[1][i]);
+  }
+}
+
+}  // namespace internal
+}  // namespace ceres