// Ceres Solver - A fast non-linear least squares minimizer

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// http://ceres-solver.org/

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// 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;

}