diff --git a/examples/curve_fitting.cc b/examples/curve_fitting.cc
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+++ b/examples/curve_fitting.cc
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+// 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: sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/ceres.h"
+#include "glog/logging.h"
+
+using ceres::AutoDiffCostFunction;
+using ceres::CostFunction;
+using ceres::Problem;
+using ceres::Solver;
+using ceres::Solve;
+
+// Data generated using the following octave code.
+//   randn('seed', 23497);
+//   m = 0.3;
+//   c = 0.1;
+//   x=[0:0.075:5];
+//   y = exp(m * x + c);
+//   noise = randn(size(x)) * 0.2;
+//   y_observed = y + noise;
+//   data = [x', y_observed'];
+
+const int kNumObservations = 67;
+const double data[] = {
+  0.000000e+00, 1.133898e+00,
+  7.500000e-02, 1.334902e+00,
+  1.500000e-01, 1.213546e+00,
+  2.250000e-01, 1.252016e+00,
+  3.000000e-01, 1.392265e+00,
+  3.750000e-01, 1.314458e+00,
+  4.500000e-01, 1.472541e+00,
+  5.250000e-01, 1.536218e+00,
+  6.000000e-01, 1.355679e+00,
+  6.750000e-01, 1.463566e+00,
+  7.500000e-01, 1.490201e+00,
+  8.250000e-01, 1.658699e+00,
+  9.000000e-01, 1.067574e+00,
+  9.750000e-01, 1.464629e+00,
+  1.050000e+00, 1.402653e+00,
+  1.125000e+00, 1.713141e+00,
+  1.200000e+00, 1.527021e+00,
+  1.275000e+00, 1.702632e+00,
+  1.350000e+00, 1.423899e+00,
+  1.425000e+00, 1.543078e+00,
+  1.500000e+00, 1.664015e+00,
+  1.575000e+00, 1.732484e+00,
+  1.650000e+00, 1.543296e+00,
+  1.725000e+00, 1.959523e+00,
+  1.800000e+00, 1.685132e+00,
+  1.875000e+00, 1.951791e+00,
+  1.950000e+00, 2.095346e+00,
+  2.025000e+00, 2.361460e+00,
+  2.100000e+00, 2.169119e+00,
+  2.175000e+00, 2.061745e+00,
+  2.250000e+00, 2.178641e+00,
+  2.325000e+00, 2.104346e+00,
+  2.400000e+00, 2.584470e+00,
+  2.475000e+00, 1.914158e+00,
+  2.550000e+00, 2.368375e+00,
+  2.625000e+00, 2.686125e+00,
+  2.700000e+00, 2.712395e+00,
+  2.775000e+00, 2.499511e+00,
+  2.850000e+00, 2.558897e+00,
+  2.925000e+00, 2.309154e+00,
+  3.000000e+00, 2.869503e+00,
+  3.075000e+00, 3.116645e+00,
+  3.150000e+00, 3.094907e+00,
+  3.225000e+00, 2.471759e+00,
+  3.300000e+00, 3.017131e+00,
+  3.375000e+00, 3.232381e+00,
+  3.450000e+00, 2.944596e+00,
+  3.525000e+00, 3.385343e+00,
+  3.600000e+00, 3.199826e+00,
+  3.675000e+00, 3.423039e+00,
+  3.750000e+00, 3.621552e+00,
+  3.825000e+00, 3.559255e+00,
+  3.900000e+00, 3.530713e+00,
+  3.975000e+00, 3.561766e+00,
+  4.050000e+00, 3.544574e+00,
+  4.125000e+00, 3.867945e+00,
+  4.200000e+00, 4.049776e+00,
+  4.275000e+00, 3.885601e+00,
+  4.350000e+00, 4.110505e+00,
+  4.425000e+00, 4.345320e+00,
+  4.500000e+00, 4.161241e+00,
+  4.575000e+00, 4.363407e+00,
+  4.650000e+00, 4.161576e+00,
+  4.725000e+00, 4.619728e+00,
+  4.800000e+00, 4.737410e+00,
+  4.875000e+00, 4.727863e+00,
+  4.950000e+00, 4.669206e+00,
+};
+
+struct ExponentialResidual {
+  ExponentialResidual(double x, double y)
+      : x_(x), y_(y) {}
+
+  template <typename T> bool operator()(const T* const m,
+                                        const T* const c,
+                                        T* residual) const {
+    residual[0] = y_ - exp(m[0] * x_ + c[0]);
+    return true;
+  }
+
+ private:
+  const double x_;
+  const double y_;
+};
+
+int main(int argc, char** argv) {
+  google::InitGoogleLogging(argv[0]);
+
+  double m = 0.0;
+  double c = 0.0;
+
+  Problem problem;
+  for (int i = 0; i < kNumObservations; ++i) {
+    problem.AddResidualBlock(
+        new AutoDiffCostFunction<ExponentialResidual, 1, 1, 1>(
+            new ExponentialResidual(data[2 * i], data[2 * i + 1])),
+        NULL,
+        &m, &c);
+  }
+
+  Solver::Options options;
+  options.max_num_iterations = 25;
+  options.linear_solver_type = ceres::DENSE_QR;
+  options.minimizer_progress_to_stdout = true;
+
+  Solver::Summary summary;
+  Solve(options, &problem, &summary);
+  std::cout << summary.BriefReport() << "\n";
+  std::cout << "Initial m: " << 0.0 << " c: " << 0.0 << "\n";
+  std::cout << "Final   m: " << m << " c: " << c << "\n";
+  return 0;
+}