diff --git a/internal/ceres/gradient_checking_cost_function.cc b/internal/ceres/gradient_checking_cost_function.cc
new file mode 100644
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--- /dev/null
+++ b/internal/ceres/gradient_checking_cost_function.cc
@@ -0,0 +1,308 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 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: keir@google.com (Keir Mierle)
+
+#include "ceres/gradient_checking_cost_function.h"
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#include <glog/logging.h>
+#include "ceres/parameter_block.h"
+#include "ceres/problem_impl.h"
+#include "ceres/program.h"
+#include "ceres/residual_block.h"
+#include "ceres/runtime_numeric_diff_cost_function.h"
+#include "ceres/stringprintf.h"
+#include "ceres/cost_function.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/problem.h"
+#include "ceres/types.h"
+
+namespace ceres {
+namespace internal {
+namespace {
+
+// True if x and y have an absolute relative difference less than
+// relative_precision and false otherwise. Stores the relative and absolute
+// difference in relative/absolute_error if non-NULL.
+bool IsClose(double x, double y, double relative_precision,
+             double *relative_error,
+             double *absolute_error) {
+  double local_absolute_error;
+  double local_relative_error;
+  if (!absolute_error) {
+    absolute_error = &local_absolute_error;
+  }
+  if (!relative_error) {
+    relative_error = &local_relative_error;
+  }
+  *absolute_error = fabs(x - y);
+  *relative_error = *absolute_error / max(fabs(x), fabs(y));
+  if (x == 0 || y == 0) {
+    // If x or y is exactly zero, then relative difference doesn't have any
+    // meaning. Take the absolute difference instead.
+    *relative_error = *absolute_error;
+  }
+  return fabs(*relative_error) < fabs(relative_precision);
+}
+
+class GradientCheckingCostFunction : public CostFunction {
+ public:
+  GradientCheckingCostFunction(const CostFunction* function,
+                               double relative_step_size,
+                               double relative_precision,
+                               const string& extra_info)
+      : function_(function),
+        finite_diff_cost_function_(
+            CreateRuntimeNumericDiffCostFunction(function,
+                                                 CENTRAL,
+                                                 relative_step_size)),
+        relative_precision_(relative_precision),
+        extra_info_(extra_info) {
+    *mutable_parameter_block_sizes() = function->parameter_block_sizes();
+    set_num_residuals(function->num_residuals());
+  }
+
+  virtual ~GradientCheckingCostFunction() { }
+
+  virtual bool Evaluate(double const* const* parameters,
+                        double* residuals,
+                        double** jacobians) const {
+    if (!jacobians) {
+      // Nothing to check in this case; just forward.
+      return function_->Evaluate(parameters, residuals, NULL);
+    }
+
+    int num_residuals = function_->num_residuals();
+
+    // Make space for the jacobians of the two methods.
+    const vector<int16>& block_sizes = function_->parameter_block_sizes();
+    vector<Matrix> term_jacobians(block_sizes.size());
+    vector<Matrix> finite_difference_jacobians(block_sizes.size());
+    vector<double*> term_jacobian_pointers(block_sizes.size());
+    vector<double*> finite_difference_jacobian_pointers(block_sizes.size());
+    for (int i = 0; i < block_sizes.size(); i++) {
+      term_jacobians[i].resize(num_residuals, block_sizes[i]);
+      term_jacobian_pointers[i] = term_jacobians[i].data();
+      finite_difference_jacobians[i].resize(num_residuals, block_sizes[i]);
+      finite_difference_jacobian_pointers[i] =
+          finite_difference_jacobians[i].data();
+    }
+
+    // Evaluate the derivative using the user supplied code.
+    if (!function_->Evaluate(parameters,
+                             residuals,
+                             &term_jacobian_pointers[0])) {
+      LOG(WARNING) << "Function evaluation failed.";
+      return false;
+    }
+
+    // Evaluate the derivative using numeric derivatives.
+    finite_diff_cost_function_->Evaluate(
+        parameters,
+        residuals,
+        &finite_difference_jacobian_pointers[0]);
+
+    // See if any elements have relative error larger than the threshold.
+    int num_bad_jacobian_components = 0;
+    double worst_relative_error = 0;
+
+    // Accumulate the error message for all the jacobians, since it won't get
+    // output if there are no bad jacobian components.
+    string m;
+    for (int k = 0; k < block_sizes.size(); k++) {
+      // Copy the original jacobian blocks into the jacobians array.
+      if (jacobians[k] != NULL) {
+        MatrixRef(jacobians[k],
+                  term_jacobians[k].rows(),
+                  term_jacobians[k].cols()) = term_jacobians[k];
+      }
+
+      StringAppendF(&m,
+                    "========== "
+                    "Jacobian for " "block %d: (%ld by %ld)) "
+                    "==========\n",
+                    k,
+                    term_jacobians[k].rows(),
+                    term_jacobians[k].cols());
+      // The funny spacing creates appropriately aligned column headers.
+      m += " block  row  col        user dx/dy    num diff dx/dy         "
+           "abs error    relative error         parameter          residual\n";
+
+      for (int i = 0; i < term_jacobians[k].rows(); i++) {
+        for (int j = 0; j < term_jacobians[k].cols(); j++) {
+          double term_jacobian = term_jacobians[k](i, j);
+          double finite_jacobian = finite_difference_jacobians[k](i, j);
+          double relative_error, absolute_error;
+          bool bad_jacobian_entry =
+              !IsClose(term_jacobian,
+                       finite_jacobian,
+                       relative_precision_,
+                       &relative_error,
+                       &absolute_error);
+          worst_relative_error = std::max(worst_relative_error,
+                                          relative_error);
+
+          StringAppendF(&m, "%6d %4d %4d %17g %17g %17g %17g %17g %17g",
+                        k, i, j,
+                        term_jacobian, finite_jacobian,
+                        absolute_error, relative_error,
+                        parameters[k][j],
+                        residuals[i]);
+
+          if (bad_jacobian_entry) {
+            num_bad_jacobian_components++;
+            StringAppendF(
+                &m, " ------ (%d,%d,%d) Relative error worse than %g",
+                k, i, j, relative_precision_);
+          }
+          m += "\n";
+        }
+      }
+    }
+
+    // Since there were some bad errors, dump comprehensive debug info.
+    if (num_bad_jacobian_components) {
+      string header = StringPrintf("Detected %d bad jacobian component(s). "
+                                   "Worst relative error was %g.\n",
+                                   num_bad_jacobian_components,
+                                   worst_relative_error);
+      if (!extra_info_.empty()) {
+        header += "Extra info for this residual: " + extra_info_ + "\n";
+      }
+      LOG(WARNING) << "\n" << header << m;
+    }
+    return true;
+  }
+
+ private:
+  const CostFunction* function_;
+  internal::scoped_ptr<CostFunction> finite_diff_cost_function_;
+  double relative_precision_;
+  string extra_info_;
+};
+
+}  // namespace
+
+CostFunction *CreateGradientCheckingCostFunction(
+    const CostFunction *cost_function,
+    double relative_step_size,
+    double relative_precision,
+    const string& extra_info) {
+  return new GradientCheckingCostFunction(cost_function,
+                                          relative_step_size,
+                                          relative_precision,
+                                          extra_info);
+}
+
+ProblemImpl* CreateGradientCheckingProblemImpl(ProblemImpl* problem_impl,
+                                               double relative_step_size,
+                                               double relative_precision) {
+  // We create new CostFunctions by wrapping the original CostFunction
+  // in a gradient checking CostFunction. So its okay for the
+  // ProblemImpl to take ownership of it and destroy it. The
+  // LossFunctions and LocalParameterizations are reused and since
+  // they are owned by problem_impl, gradient_checking_problem_impl
+  // should not take ownership of it.
+  Problem::Options gradient_checking_problem_options;
+  gradient_checking_problem_options.cost_function_ownership = TAKE_OWNERSHIP;
+  gradient_checking_problem_options.loss_function_ownership =
+      DO_NOT_TAKE_OWNERSHIP;
+  gradient_checking_problem_options.local_parameterization_ownership =
+      DO_NOT_TAKE_OWNERSHIP;
+
+  ProblemImpl* gradient_checking_problem_impl = new ProblemImpl(
+      gradient_checking_problem_options);
+
+  Program* program = problem_impl->mutable_program();
+
+  // For every ParameterBlock in problem_impl, create a new parameter
+  // block with the same local parameterization and constancy.
+  const vector<ParameterBlock*>& parameter_blocks = program->parameter_blocks();
+  for (int i = 0; i < parameter_blocks.size(); ++i) {
+    ParameterBlock* parameter_block = parameter_blocks[i];
+    gradient_checking_problem_impl->AddParameterBlock(
+        parameter_block->mutable_user_state(),
+        parameter_block->Size(),
+        parameter_block->mutable_local_parameterization());
+
+    if (parameter_block->IsConstant()) {
+      gradient_checking_problem_impl->SetParameterBlockConstant(
+          parameter_block->mutable_user_state());
+    }
+  }
+
+  // For every ResidualBlock in problem_impl, create a new
+  // ResidualBlock by wrapping its CostFunction inside a
+  // GradientCheckingCostFunction.
+  const vector<ResidualBlock*>& residual_blocks = program->residual_blocks();
+  for (int i = 0; i < residual_blocks.size(); ++i) {
+    ResidualBlock* residual_block = residual_blocks[i];
+
+    // Build a human readable string which identifies the
+    // ResidualBlock. This is used by the GradientCheckingCostFunction
+    // when logging debugging information.
+    string extra_info = StringPrintf(
+        "Residual block id %d; depends on parameters [", i);
+    vector<double*> parameter_blocks;
+    for (int j = 0; j < residual_block->NumParameterBlocks(); ++j) {
+      ParameterBlock* parameter_block = residual_block->parameter_blocks()[j];
+      parameter_blocks.push_back(parameter_block->mutable_user_state());
+      StringAppendF(&extra_info, "%p", parameter_block->mutable_user_state());
+      extra_info += (j < residual_block->NumParameterBlocks() - 1) ? ", " : "]";
+    }
+
+    // Wrap the original CostFunction in a GradientCheckingCostFunction.
+    CostFunction* gradient_checking_cost_function =
+        CreateGradientCheckingCostFunction(residual_block->cost_function(),
+                                           relative_step_size,
+                                           relative_precision,
+                                           extra_info);
+
+    // The const_cast is necessary because
+    // ProblemImpl::AddResidualBlock can potentially take ownership of
+    // the LossFunction, but in this case we are guaranteed that this
+    // will not be the case, so this const_cast is harmless.
+    gradient_checking_problem_impl->AddResidualBlock(
+        gradient_checking_cost_function,
+        const_cast<LossFunction*>(residual_block->loss_function()),
+        parameter_blocks);
+  }
+
+  return gradient_checking_problem_impl;
+}
+
+
+}  // namespace internal
+}  // namespace ceres