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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 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: strandmark@google.com (Petter Strandmark)
#include "ceres/gradient_problem_solver.h"
#include "ceres/gradient_problem.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:
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 != nullptr) {
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 {
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 != nullptr) {
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) {}
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 (double value : callback.x_values) {
EXPECT_EQ(50.0, value);
}
// 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
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