| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2019 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 materils 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/invert_psd_matrix.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| template <int kSize> |
| void BenchmarkFixedSizedInvertPSDMatrix(benchmark::State& state) { |
| using MatrixType = typename EigenTypes<kSize, kSize>::Matrix; |
| MatrixType input = MatrixType::Random(); |
| input += input.transpose() + MatrixType::Identity(); |
| |
| MatrixType output; |
| constexpr bool kAssumeFullRank = true; |
| for (auto _ : state) { |
| benchmark::DoNotOptimize( |
| output = InvertPSDMatrix<kSize>(kAssumeFullRank, input)); |
| } |
| } |
| |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 1); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 2); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 3); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 4); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 5); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 6); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 7); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 8); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 9); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 10); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 11); |
| BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 12); |
| |
| void BenchmarkDynamicallyInvertPSDMatrix(benchmark::State& state) { |
| using MatrixType = |
| typename EigenTypes<Eigen::Dynamic, Eigen::Dynamic>::Matrix; |
| const int size = state.range(0); |
| MatrixType input = MatrixType::Random(size, size); |
| input += input.transpose() + MatrixType::Identity(size, size); |
| |
| MatrixType output; |
| constexpr bool kAssumeFullRank = true; |
| for (auto _ : state) { |
| benchmark::DoNotOptimize( |
| output = InvertPSDMatrix<Eigen::Dynamic>(kAssumeFullRank, input)); |
| } |
| } |
| |
| BENCHMARK(BenchmarkDynamicallyInvertPSDMatrix) |
| ->Apply([](benchmark::internal::Benchmark* benchmark) { |
| for (int i = 1; i < 13; ++i) { |
| benchmark->Args({i}); |
| } |
| }); |
| |
| } // namespace internal |
| } // namespace ceres |
| |
| BENCHMARK_MAIN(); |