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-// 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)
-//
-// A simple example of using the Ceres minimizer.
-//
-// Minimize 0.5 (10 - x)^2 using analytic jacobian matrix.
-
-#include <vector>
-#include "ceres/ceres.h"
-#include "gflags/gflags.h"
-#include "glog/logging.h"
-
-using ceres::SizedCostFunction;
-using ceres::Problem;
-using ceres::Solver;
-using ceres::Solve;
-
-class SimpleCostFunction
-  : public SizedCostFunction<1 /* number of residuals */,
-                             1 /* size of first parameter */> {
- public:
-  virtual ~SimpleCostFunction() {}
-  virtual bool Evaluate(double const* const* parameters,
-                        double* residuals,
-                        double** jacobians) const {
-    double x = parameters[0][0];
-
-    // f(x) = 10 - x.
-    residuals[0] = 10 - x;
-
-    // f'(x) = -1. Since there's only 1 parameter and that parameter
-    // has 1 dimension, there is only 1 element to fill in the
-    // jacobians.
-    if (jacobians != NULL && jacobians[0] != NULL) {
-      jacobians[0][0] = -1;
-    }
-    return true;
-  }
-};
-
-int main(int argc, char** argv) {
-  google::ParseCommandLineFlags(&argc, &argv, true);
-  google::InitGoogleLogging(argv[0]);
-
-  // The variable with its initial value that we will be solving for.
-  double x = 5.0;
-
-  // Build the problem.
-  Problem problem;
-  // Set up the only cost function (also known as residual).
-  problem.AddResidualBlock(new SimpleCostFunction, NULL, &x);
-
-  // Run the solver!
-  Solver::Options options;
-  options.max_num_iterations = 10;
-  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 << "x : 5.0 -> " << x << "\n";
-  return 0;
-}