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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: keir@google.com (Keir Mierle)
+//
+// A simple example of using the Ceres minimizer.
+//
+// Minimize 0.5 (10 - x)^2 using jacobian matrix computed using
+// automatic differentiation.
+
+#include "ceres/ceres.h"
+#include "glog/logging.h"
+
+using ceres::AutoDiffCostFunction;
+using ceres::CostFunction;
+using ceres::Problem;
+using ceres::Solver;
+using ceres::Solve;
+
+// A templated cost functor that implements the residual r = 10 -
+// x. The method operator() is templated so that we can then use an
+// automatic differentiation wrapper around it to generate its
+// derivatives.
+struct CostFunctor {
+  template <typename T> bool operator()(const T* const x, T* residual) const {
+    residual[0] = 10.0 - x[0];
+    return true;
+  }
+};
+
+int main(int argc, char** argv) {
+  google::InitGoogleLogging(argv[0]);
+
+  // The variable to solve for with its initial value. It will be
+  // mutated in place by the solver.
+  double x = 0.5;
+  const double initial_x = x;
+
+  // Build the problem.
+  Problem problem;
+
+  // Set up the only cost function (also known as residual). This uses
+  // auto-differentiation to obtain the derivative (jacobian).
+  CostFunction* cost_function =
+      new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
+  problem.AddResidualBlock(cost_function, NULL, &x);
+
+  // Run the solver!
+  Solver::Options options;
+  options.minimizer_progress_to_stdout = true;
+  Solver::Summary summary;
+  Solve(options, &problem, &summary);
+
+  std::cout << summary.BriefReport() << "\n";
+  std::cout << "x : " << initial_x
+            << " -> " << x << "\n";
+  return 0;
+}