| // 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 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 | 
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 | // 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(); |