| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2023 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 "Eigen/Cholesky" |
| #include "ceres/block_sparse_matrix.h" |
| #include "ceres/casts.h" |
| #include "ceres/context_impl.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 { |
| |
| // TODO(sameeragarwal): These tests needs to be re-written, since |
| // SparseNormalCholeskySolver is a composition of two classes now, |
| // InnerProductComputer and SparseCholesky. |
| // |
| // So the test should exercise the composition, rather than the |
| // numerics of the solver, which are well covered by tests for those |
| // classes. |
| class SparseNormalCholeskySolverTest : public ::testing::Test { |
| protected: |
| void SetUp() final { |
| std::unique_ptr<LinearLeastSquaresProblem> problem = |
| CreateLinearLeastSquaresProblemFromId(2); |
| |
| CHECK(problem != nullptr); |
| A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release())); |
| b_ = std::move(problem->b); |
| D_ = std::move(problem->D); |
| } |
| |
| void TestSolver(const LinearSolver::Options& options, double* D) { |
| Matrix dense_A; |
| A_->ToDenseMatrix(&dense_A); |
| Matrix lhs = dense_A.transpose() * dense_A; |
| if (D != nullptr) { |
| lhs += (ConstVectorRef(D, A_->num_cols()).array() * |
| ConstVectorRef(D, A_->num_cols()).array()) |
| .matrix() |
| .asDiagonal(); |
| } |
| |
| Vector rhs(A_->num_cols()); |
| rhs.setZero(); |
| A_->LeftMultiplyAndAccumulate(b_.get(), rhs.data()); |
| Vector expected_solution = lhs.llt().solve(rhs); |
| |
| std::unique_ptr<LinearSolver> solver(LinearSolver::Create(options)); |
| LinearSolver::PerSolveOptions per_solve_options; |
| per_solve_options.D = D; |
| Vector actual_solution(A_->num_cols()); |
| LinearSolver::Summary summary; |
| summary = solver->Solve( |
| A_.get(), b_.get(), per_solve_options, actual_solution.data()); |
| |
| EXPECT_EQ(summary.termination_type, LinearSolverTerminationType::SUCCESS); |
| |
| const double eps = options.use_mixed_precision_solves ? 2e-6 : 1e-8; |
| for (int i = 0; i < A_->num_cols(); ++i) { |
| EXPECT_NEAR(expected_solution(i), actual_solution(i), eps) |
| << "\nExpected: " << expected_solution.transpose() |
| << "\nActual: " << actual_solution.transpose(); |
| } |
| } |
| |
| void TestSolver(const LinearSolver::Options& options) { |
| TestSolver(options, nullptr); |
| TestSolver(options, D_.get()); |
| } |
| |
| std::unique_ptr<BlockSparseMatrix> A_; |
| std::unique_ptr<double[]> b_; |
| std::unique_ptr<double[]> D_; |
| }; |
| |
| #ifndef CERES_NO_SUITESPARSE |
| TEST_F(SparseNormalCholeskySolverTest, |
| SparseNormalCholeskyUsingSuiteSparsePreOrdering) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = SUITE_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::NATURAL; |
| ContextImpl context; |
| options.context = &context; |
| TestSolver(options); |
| } |
| |
| TEST_F(SparseNormalCholeskySolverTest, |
| SparseNormalCholeskyUsingSuiteSparsePostOrdering) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = SUITE_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::AMD; |
| ContextImpl context; |
| options.context = &context; |
| TestSolver(options); |
| } |
| #endif |
| |
| #ifndef CERES_NO_ACCELERATE_SPARSE |
| TEST_F(SparseNormalCholeskySolverTest, |
| SparseNormalCholeskyUsingAccelerateSparsePreOrdering) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = ACCELERATE_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::NATURAL; |
| ContextImpl context; |
| options.context = &context; |
| TestSolver(options); |
| } |
| |
| TEST_F(SparseNormalCholeskySolverTest, |
| SparseNormalCholeskyUsingAcceleratePostOrdering) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = ACCELERATE_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::AMD; |
| ContextImpl context; |
| options.context = &context; |
| TestSolver(options); |
| } |
| #endif |
| |
| #ifdef CERES_USE_EIGEN_SPARSE |
| TEST_F(SparseNormalCholeskySolverTest, |
| SparseNormalCholeskyUsingEigenPreOrdering) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = EIGEN_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::NATURAL; |
| ContextImpl context; |
| options.context = &context; |
| TestSolver(options); |
| } |
| |
| TEST_F(SparseNormalCholeskySolverTest, |
| SparseNormalCholeskyUsingEigenPostOrdering) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = EIGEN_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::AMD; |
| ContextImpl context; |
| options.context = &context; |
| TestSolver(options); |
| } |
| #endif // CERES_USE_EIGEN_SPARSE |
| |
| #ifndef CERES_NO_CUDSS |
| TEST_F(SparseNormalCholeskySolverTest, SparseNormalCholeskyUsingCuDSSSingle) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = CUDA_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::AMD; |
| options.use_mixed_precision_solves = true; |
| ContextImpl context; |
| options.context = &context; |
| std::string error; |
| CHECK(context.InitCuda(&error)) << error; |
| TestSolver(options); |
| } |
| |
| TEST_F(SparseNormalCholeskySolverTest, SparseNormalCholeskyUsingCuDSSDouble) { |
| LinearSolver::Options options; |
| options.sparse_linear_algebra_library_type = CUDA_SPARSE; |
| options.type = SPARSE_NORMAL_CHOLESKY; |
| options.ordering_type = OrderingType::AMD; |
| ContextImpl context; |
| options.context = &context; |
| std::string error; |
| CHECK(context.InitCuda(&error)) << error; |
| TestSolver(options); |
| } |
| #endif // CERES_USE_EIGEN_SPARSE |
| |
| } // namespace ceres::internal |