internal/ceres/gradient_problem_solver_test.cc

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
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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() {}

  bool Evaluate(const double* parameters,
                double* cost,
                double* gradient) const final {
    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;
  }

  int NumParameters() const final { 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);
}

class QuadraticFunction : public ceres::FirstOrderFunction {
  virtual ~QuadraticFunction() {}
  bool Evaluate(const double* parameters,
                double* cost,
                double* gradient) const final {
    const double x = parameters[0];
    *cost = 0.5 * (5.0 - x) * (5.0 - x);
    if (gradient != NULL) {
      gradient[0] = x - 5.0;
    }

    return true;
  }
  int NumParameters() const final { return 1; }
};

struct RememberingCallback : public IterationCallback {
  explicit RememberingCallback(double *x) : calls(0), x(x) {}
  virtual ~RememberingCallback() {}
  CallbackReturnType operator()(const IterationSummary& summary) final {
    x_values.push_back(*x);
    return SOLVER_CONTINUE;
  }
  int calls;
  double *x;
  std::vector<double> x_values;
};


TEST(Solver, UpdateStateEveryIterationOption) {
  double x = 50.0;
  const double original_x = x;

  ceres::GradientProblem problem(new QuadraticFunction);
  ceres::GradientProblemSolver::Options options;
  RememberingCallback callback(&x);
  options.callbacks.push_back(&callback);
  ceres::GradientProblemSolver::Summary summary;

  int num_iterations;

  // First try: no updating.
  ceres::Solve(options, problem, &x, &summary);
  num_iterations = summary.iterations.size() - 1;
  EXPECT_GT(num_iterations, 1);
  for (int i = 0; i < callback.x_values.size(); ++i) {
    EXPECT_EQ(50.0, callback.x_values[i]);
  }

  // Second try: with updating
  x = 50.0;
  options.update_state_every_iteration = true;
  callback.x_values.clear();
  ceres::Solve(options, problem, &x, &summary);
  num_iterations = summary.iterations.size() - 1;
  EXPECT_GT(num_iterations, 1);
  EXPECT_EQ(original_x, callback.x_values[0]);
  EXPECT_NE(original_x, callback.x_values[1]);
}

}  // namespace internal
}  // namespace ceres