// 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 automatically computed derivatives.

#include "absl/log/initialize.h"
#include "ceres/ceres.h"

// f(x,y) = (1-x)^2 + 100(y - x^2)^2;
struct Rosenbrock {
  template <typename T>
  bool operator()(const T* parameters, T* cost) const {
    const T x = parameters[0];
    const T y = parameters[1];
    cost[0] = (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);
    return true;
  }

  static std::unique_ptr<ceres::FirstOrderFunction> Create() {
    constexpr int kNumParameters = 2;
    return std::make_unique<
        ceres::AutoDiffFirstOrderFunction<Rosenbrock, kNumParameters>>();
  }
};

int main(int argc, char** argv) {
  absl::InitializeLog();

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

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

  ceres::GradientProblemSolver::Summary summary;
  ceres::GradientProblem problem(Rosenbrock::Create());
  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;
}