|  | // 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. | 
|  | // | 
|  | // Authors: sameeragarwal@google.com (Sameer Agarwal) | 
|  |  | 
|  | #include "Eigen/Dense" | 
|  | #include "benchmark/benchmark.h" | 
|  | #include "ceres/context_impl.h" | 
|  | #include "ceres/dense_sparse_matrix.h" | 
|  | #include "ceres/internal/config.h" | 
|  | #include "ceres/linear_solver.h" | 
|  |  | 
|  | namespace ceres::internal { | 
|  |  | 
|  | template <ceres::DenseLinearAlgebraLibraryType kLibraryType, | 
|  | ceres::LinearSolverType kSolverType> | 
|  | static void BM_DenseSolver(benchmark::State& state) { | 
|  | const int num_rows = state.range(0); | 
|  | const int num_cols = state.range(1); | 
|  | DenseSparseMatrix jacobian(num_rows, num_cols); | 
|  | *jacobian.mutable_matrix() = Eigen::MatrixXd::Random(num_rows, num_cols); | 
|  | Eigen::VectorXd rhs = Eigen::VectorXd::Random(num_rows, 1); | 
|  |  | 
|  | Eigen::VectorXd solution(num_cols); | 
|  |  | 
|  | LinearSolver::Options options; | 
|  | options.type = kSolverType; | 
|  | options.dense_linear_algebra_library_type = kLibraryType; | 
|  | ContextImpl context; | 
|  | options.context = &context; | 
|  | auto solver = LinearSolver::Create(options); | 
|  |  | 
|  | LinearSolver::PerSolveOptions per_solve_options; | 
|  | Eigen::VectorXd diagonal = Eigen::VectorXd::Ones(num_cols) * 100; | 
|  | per_solve_options.D = diagonal.data(); | 
|  | for (auto _ : state) { | 
|  | solver->Solve(&jacobian, rhs.data(), per_solve_options, solution.data()); | 
|  | } | 
|  | } | 
|  |  | 
|  | // Some reasonable matrix sizes. I picked them out of thin air. | 
|  | static void MatrixSizes(benchmark::internal::Benchmark* b) { | 
|  | // {num_rows, num_cols} | 
|  | b->Args({1, 1}); | 
|  | b->Args({2, 1}); | 
|  | b->Args({3, 1}); | 
|  | b->Args({6, 2}); | 
|  | b->Args({10, 3}); | 
|  | b->Args({12, 4}); | 
|  | b->Args({20, 5}); | 
|  | b->Args({40, 5}); | 
|  | b->Args({100, 10}); | 
|  | b->Args({150, 15}); | 
|  | b->Args({200, 16}); | 
|  | b->Args({225, 18}); | 
|  | b->Args({300, 20}); | 
|  | b->Args({400, 20}); | 
|  | b->Args({600, 22}); | 
|  | b->Args({800, 25}); | 
|  | } | 
|  |  | 
|  | BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::EIGEN, ceres::DENSE_QR) | 
|  | ->Apply(MatrixSizes); | 
|  | BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::EIGEN, ceres::DENSE_NORMAL_CHOLESKY) | 
|  | ->Apply(MatrixSizes); | 
|  |  | 
|  | #ifndef CERES_NO_LAPACK | 
|  | BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::LAPACK, ceres::DENSE_QR) | 
|  | ->Apply(MatrixSizes); | 
|  | BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::LAPACK, ceres::DENSE_NORMAL_CHOLESKY) | 
|  | ->Apply(MatrixSizes); | 
|  | #endif  // CERES_NO_LAPACK | 
|  |  | 
|  | #ifndef CERES_NO_CUDA | 
|  | BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::CUDA, ceres::DENSE_NORMAL_CHOLESKY) | 
|  | ->Apply(MatrixSizes); | 
|  | BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::CUDA, ceres::DENSE_QR) | 
|  | ->Apply(MatrixSizes); | 
|  | #endif  // CERES_NO_CUDA | 
|  |  | 
|  | }  // namespace ceres::internal | 
|  |  | 
|  | BENCHMARK_MAIN(); |