| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990 | // 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 ceresBENCHMARK_MAIN();
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