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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 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
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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//
// Author: strandmark@google.com (Petter Strandmark)

#include "ceres/gradient_problem.h"
#include "ceres/gradient_problem_solver.h"

#include "gtest/gtest.h"

namespace ceres {
namespace internal {

// Rosenbrock function; see http://en.wikipedia.org/wiki/Rosenbrock_function .
class Rosenbrock : public ceres::FirstOrderFunction {
 public:
  virtual ~Rosenbrock() {}

  virtual bool Evaluate(const double* parameters,
                        double* cost,
                        double* gradient) 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);
    if (gradient != NULL) {
      gradient[0] = -2.0 * (1.0 - x) - 200.0 * (y - x * x) * 2.0 * x;
      gradient[1] = 200.0 * (y - x * x);
    }
    return true;
  }

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

TEST(GradientProblemSolver, SolvesRosenbrockWithDefaultOptions) {
  const double expected_tolerance = 1e-9;
  double parameters[2] = {-1.2, 0.0};

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

  EXPECT_EQ(CONVERGENCE, summary.termination_type);
  EXPECT_NEAR(1.0, parameters[0], expected_tolerance);
  EXPECT_NEAR(1.0, parameters[1], expected_tolerance);
}

}  // namespace internal
}  // namespace ceres