// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 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: sameeragarwal@google.com (Sameer Agarwal)
//
// Example of minimizing the Rosenbrock function
// (https://en.wikipedia.org/wiki/Rosenbrock_function) using
// GradientProblemSolver using derivatives computed using numeric
// differentiation.

#include <iostream>
#include <memory>

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

// f(x,y) = (1-x)^2 + 100(y - x^2)^2;
struct Rosenbrock {
  bool operator()(const double* parameters, double* cost) const {
    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);
    return true;
  }

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