| // 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::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 ceres::internal |