// Ceres Solver - A fast non-linear least squares minimizer
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// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// Example of minimizing the Rosenbrock function
// (https://en.wikipedia.org/wiki/Rosenbrock_function) using
// GradientProblemSolver using analytic derivatives.

#include "ceres/ceres.h"
#include "glog/logging.h"

// f(x,y) = (1-x)^2 + 100(y - x^2)^2;
class Rosenbrock final : public ceres::FirstOrderFunction {
 public:
  bool Evaluate(const double* parameters,
                double* cost,
                double* gradient) const override {
    const double x = parameters[0];
    const double y = parameters[1];

    cost[0] = (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);

    if (gradient) {
      gradient[0] = -2.0 * (1.0 - x) - 200.0 * (y - x * x) * 2.0 * x;
      gradient[1] = 200.0 * (y - x * x);
    }

    return true;
  }

  int NumParameters() const override { return 2; }
};

int main(int argc, char** argv) {
  google::InitGoogleLogging(argv[0]);

  double parameters[2] = {-1.2, 1.0};

  ceres::GradientProblemSolver::Options options;
  options.minimizer_progress_to_stdout = true;

  ceres::GradientProblemSolver::Summary summary;
  ceres::GradientProblem problem(new Rosenbrock());
  ceres::Solve(options, problem, parameters, &summary);

  std::cout << summary.FullReport() << "\n";
  std::cout << "Initial x: " << -1.2 << " y: " << 1.0 << "\n";
  std::cout << "Final   x: " << parameters[0] << " y: " << parameters[1]
            << "\n";
  return 0;
}