1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
|
// 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);
static 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();
|
