diff --git a/include/ceres/dynamic_numeric_diff_cost_function.h b/include/ceres/dynamic_numeric_diff_cost_function.h
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+// 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 numeric diff implementation differs from the one found in
+// numeric_diff_cost_function.h by supporting numericdiff 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
+// numeric diff; the expected interface for the cost functors is:
+//
+//   struct MyCostFunctor {
+//     template<typename T>
+//     bool operator()(double const* const* parameters, double* 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
+// DynamicNumericDiffCostFunction. For example:
+//
+//   DynamicAutoDiffCostFunction<MyCostFunctor, CENTRAL> cost_function(
+//       new MyCostFunctor());
+//   cost_function.AddParameterBlock(5);
+//   cost_function.AddParameterBlock(10);
+//   cost_function.SetNumResiduals(21);
+
+#ifndef CERES_PUBLIC_DYNAMIC_NUMERIC_DIFF_COST_FUNCTION_H_
+#define CERES_PUBLIC_DYNAMIC_NUMERIC_DIFF_COST_FUNCTION_H_
+
+#include <cmath>
+#include <numeric>
+#include <vector>
+
+#include "ceres/cost_function.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/internal/eigen.h"
+#include "glog/logging.h"
+
+namespace ceres {
+
+template <typename CostFunctor, NumericDiffMethod method = CENTRAL>
+class DynamicNumericDiffCostFunction : public CostFunction {
+ public:
+  explicit DynamicNumericDiffCostFunction(CostFunctor* functor,
+                                          Ownership ownership = TAKE_OWNERSHIP,
+                                          double relative_step_size = 1e-6)
+      : functor_(functor),
+        ownership_(ownership),
+        relative_step_size_(relative_step_size) {
+  }
+
+  virtual ~DynamicNumericDiffCostFunction() {
+    if (ownership_ != TAKE_OWNERSHIP) {
+      functor_.release();
+    }
+  }
+
+  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 DynamicNumericDiffCostFunction::SetNumResiduals() "
+        << "before DynamicNumericDiffCostFunction::Evaluate().";
+
+    const vector<int16>& block_sizes = parameter_block_sizes();
+    CHECK(!block_sizes.empty())
+        << "You must call DynamicNumericDiffCostFunction::AddParameterBlock() "
+        << "before DynamicNumericDiffCostFunction::Evaluate().";
+
+    bool status = (*functor_)(parameters, residuals);
+    if (jacobians == NULL) {
+      return status;
+    }
+
+    // Create local space for a copy of the parameters which will get mutated.
+    int parameters_size = accumulate(block_sizes.begin(), block_sizes.end(), 0);
+    vector<double> parameters_copy(parameters_size);
+    vector<double*> parameters_references_copy(block_sizes.size());
+    parameters_references_copy[0] = &parameters_copy[0];
+    for (int block = 1; block < block_sizes.size(); ++block) {
+      parameters_references_copy[block] = parameters_references_copy[block - 1]
+          + block_sizes[block - 1];
+    }
+
+    // Copy the parameters into the local temp space.
+    for (int block = 0; block < block_sizes.size(); ++block) {
+      memcpy(parameters_references_copy[block],
+             parameters[block],
+             block_sizes[block] * sizeof(*parameters[block]));
+    }
+
+    for (int block = 0; block < block_sizes.size(); ++block) {
+      if (jacobians[block] != NULL &&
+          !EvaluateJacobianForParameterBlock(block_sizes[block],
+                                             block,
+                                             relative_step_size_,
+                                             residuals,
+                                             &parameters_references_copy[0],
+                                             jacobians)) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+ private:
+  bool EvaluateJacobianForParameterBlock(const int parameter_block_size,
+                                         const int parameter_block,
+                                         const double relative_step_size,
+                                         double const* residuals_at_eval_point,
+                                         double** parameters,
+                                         double** jacobians) const {
+    using Eigen::Map;
+    using Eigen::Matrix;
+    using Eigen::Dynamic;
+    using Eigen::RowMajor;
+
+    typedef Matrix<double, Dynamic, 1> ResidualVector;
+    typedef Matrix<double, Dynamic, 1> ParameterVector;
+    typedef Matrix<double, Dynamic, Dynamic, RowMajor> JacobianMatrix;
+
+    int num_residuals = this->num_residuals();
+
+    Map<JacobianMatrix> parameter_jacobian(jacobians[parameter_block],
+                                           num_residuals,
+                                           parameter_block_size);
+
+    // Mutate one element at a time and then restore.
+    Map<ParameterVector> x_plus_delta(parameters[parameter_block],
+                                      parameter_block_size);
+    ParameterVector x(x_plus_delta);
+    ParameterVector step_size = x.array().abs() * relative_step_size;
+
+    // To handle cases where a paremeter is exactly zero, instead use
+    // the mean step_size for the other dimensions.
+    double fallback_step_size = step_size.sum() / step_size.rows();
+    if (fallback_step_size == 0.0) {
+      // If all the parameters are zero, there's no good answer. Use the given
+      // relative step_size as absolute step_size and hope for the best.
+      fallback_step_size = relative_step_size;
+    }
+
+    // For each parameter in the parameter block, use finite
+    // differences to compute the derivative for that parameter.
+    for (int j = 0; j < parameter_block_size; ++j) {
+      if (step_size(j) == 0.0) {
+        // The parameter is exactly zero, so compromise and use the
+        // mean step_size from the other parameters. This can break in
+        // many cases, but it's hard to pick a good number without
+        // problem specific knowledge.
+        step_size(j) = fallback_step_size;
+      }
+      x_plus_delta(j) = x(j) + step_size(j);
+
+      ResidualVector residuals(num_residuals);
+      if (!(*functor_)(parameters, &residuals[0])) {
+        // Something went wrong; bail.
+        return false;
+      }
+
+      // Compute this column of the jacobian in 3 steps:
+      // 1. Store residuals for the forward part.
+      // 2. Subtract residuals for the backward (or 0) part.
+      // 3. Divide out the run.
+      parameter_jacobian.col(j) = residuals;
+
+      double one_over_h = 1 / step_size(j);
+      if (method == CENTRAL) {
+        // Compute the function on the other side of x(j).
+        x_plus_delta(j) = x(j) - step_size(j);
+
+        if (!(*functor_)(parameters, &residuals[0])) {
+          // Something went wrong; bail.
+          return false;
+        }
+
+        parameter_jacobian.col(j) -= residuals;
+        one_over_h /= 2;
+      } else {
+        // Forward difference only; reuse existing residuals evaluation.
+        parameter_jacobian.col(j) -=
+            Map<const ResidualVector>(residuals_at_eval_point, num_residuals);
+      }
+      x_plus_delta(j) = x(j);  // Restore x_plus_delta.
+
+      // Divide out the run to get slope.
+      parameter_jacobian.col(j) *= one_over_h;
+    }
+    return true;
+  }
+
+  internal::scoped_ptr<CostFunctor> functor_;
+  Ownership ownership_;
+  const double relative_step_size_;
+};
+
+}  // namespace ceres
+
+#endif  // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_