| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2017 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) |
| |
| #include <memory> |
| |
| #include "ceres/casts.h" |
| #include "ceres/context_impl.h" |
| #include "ceres/internal/config.h" |
| #include "ceres/linear_least_squares_problems.h" |
| #include "ceres/linear_solver.h" |
| #include "ceres/triplet_sparse_matrix.h" |
| #include "ceres/types.h" |
| #include "glog/logging.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::internal { |
| |
| using Param = ::testing:: |
| tuple<LinearSolverType, DenseLinearAlgebraLibraryType, bool, int>; |
| |
| static std::string ParamInfoToString(testing::TestParamInfo<Param> info) { |
| Param param = info.param; |
| std::stringstream ss; |
| ss << LinearSolverTypeToString(::testing::get<0>(param)) << "_" |
| << DenseLinearAlgebraLibraryTypeToString(::testing::get<1>(param)) << "_" |
| << (::testing::get<2>(param) ? "Regularized" : "Unregularized") << "_" |
| << ::testing::get<3>(param); |
| return ss.str(); |
| } |
| |
| class DenseLinearSolverTest : public ::testing::TestWithParam<Param> {}; |
| |
| TEST_P(DenseLinearSolverTest, _) { |
| Param param = GetParam(); |
| const bool regularized = testing::get<2>(param); |
| |
| std::unique_ptr<LinearLeastSquaresProblem> problem = |
| CreateLinearLeastSquaresProblemFromId(testing::get<3>(param)); |
| DenseSparseMatrix lhs(*down_cast<TripletSparseMatrix*>(problem->A.get())); |
| |
| const int num_cols = lhs.num_cols(); |
| const int num_rows = lhs.num_rows(); |
| |
| Vector rhs = Vector::Zero(num_rows + num_cols); |
| rhs.head(num_rows) = ConstVectorRef(problem->b.get(), num_rows); |
| |
| LinearSolver::Options options; |
| options.type = ::testing::get<0>(param); |
| options.dense_linear_algebra_library_type = ::testing::get<1>(param); |
| ContextImpl context; |
| options.context = &context; |
| std::unique_ptr<LinearSolver> solver(LinearSolver::Create(options)); |
| |
| LinearSolver::PerSolveOptions per_solve_options; |
| if (regularized) { |
| per_solve_options.D = problem->D.get(); |
| } |
| |
| Vector solution(num_cols); |
| LinearSolver::Summary summary = |
| solver->Solve(&lhs, rhs.data(), per_solve_options, solution.data()); |
| EXPECT_EQ(summary.termination_type, LinearSolverTerminationType::SUCCESS); |
| |
| Vector normal_rhs = lhs.matrix().transpose() * rhs.head(num_rows); |
| Matrix normal_lhs = lhs.matrix().transpose() * lhs.matrix(); |
| |
| if (regularized) { |
| ConstVectorRef diagonal(problem->D.get(), num_cols); |
| normal_lhs += diagonal.array().square().matrix().asDiagonal(); |
| } |
| |
| Vector actual_normal_rhs = normal_lhs * solution; |
| |
| const double normalized_residual = |
| (normal_rhs - actual_normal_rhs).norm() / normal_rhs.norm(); |
| |
| EXPECT_NEAR( |
| normalized_residual, 0.0, 10 * std::numeric_limits<double>::epsilon()) |
| << "\nexpected: " << normal_rhs.transpose() |
| << "\nactual: " << actual_normal_rhs.transpose(); |
| } |
| |
| namespace { |
| |
| // TODO(sameeragarwal): Should we move away from hard coded linear |
| // least squares problem to randomly generated ones? |
| #ifndef CERES_NO_LAPACK |
| |
| INSTANTIATE_TEST_SUITE_P( |
| DenseLinearSolver, |
| DenseLinearSolverTest, |
| ::testing::Combine(::testing::Values(DENSE_QR, DENSE_NORMAL_CHOLESKY), |
| ::testing::Values(EIGEN, LAPACK), |
| ::testing::Values(true, false), |
| ::testing::Values(0, 1)), |
| ParamInfoToString); |
| |
| #else |
| |
| INSTANTIATE_TEST_SUITE_P( |
| DenseLinearSolver, |
| DenseLinearSolverTest, |
| ::testing::Combine(::testing::Values(DENSE_QR, DENSE_NORMAL_CHOLESKY), |
| ::testing::Values(EIGEN), |
| ::testing::Values(true, false), |
| ::testing::Values(0, 1)), |
| ParamInfoToString); |
| |
| #endif |
| } // namespace |
| } // namespace ceres::internal |