12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788 |
- // 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.
- //
- // Authors: sameeragarwal@google.com (Sameer Agarwal)
- #include "Eigen/Dense"
- #include "benchmark/benchmark.h"
- #include "ceres/invert_psd_matrix.h"
- namespace ceres::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);
- static void BenchmarkDynamicallyInvertPSDMatrix(benchmark::State& state) {
- using MatrixType =
- typename EigenTypes<Eigen::Dynamic, Eigen::Dynamic>::Matrix;
- const int size = static_cast<int>(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 ceres::internal
- BENCHMARK_MAIN();
|