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ceres-solver/internal/ceres/compressed_row_sparse_matrix_test.cc
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Sameer Agarwal 5a30cae583 Preparing for 2.2.0rc1 
1. Add a version history
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4. Update version strings from 2.1.0 to 2.2.0 in the docs and
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// 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.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/compressed_row_sparse_matrix.h"
#include <algorithm>
#include <memory>
#include <numeric>
#include <random>
#include <string>
#include <vector>
#include "Eigen/SparseCore"
#include "ceres/casts.h"
#include "ceres/context_impl.h"
#include "ceres/crs_matrix.h"
#include "ceres/internal/eigen.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/triplet_sparse_matrix.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres::internal {
static void CompareMatrices(const SparseMatrix* a, const SparseMatrix* b) {
EXPECT_EQ(a->num_rows(), b->num_rows());
EXPECT_EQ(a->num_cols(), b->num_cols());
int num_rows = a->num_rows();
int num_cols = a->num_cols();
for (int i = 0; i < num_cols; ++i) {
Vector x = Vector::Zero(num_cols);
x(i) = 1.0;
Vector y_a = Vector::Zero(num_rows);
Vector y_b = Vector::Zero(num_rows);
a->RightMultiplyAndAccumulate(x.data(), y_a.data());
b->RightMultiplyAndAccumulate(x.data(), y_b.data());
EXPECT_EQ((y_a - y_b).norm(), 0);
}
}
class CompressedRowSparseMatrixTest : public ::testing::Test {
protected:
void SetUp() final {
auto problem = CreateLinearLeastSquaresProblemFromId(1);
CHECK(problem != nullptr);
tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
crsm = CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm);
num_rows = tsm->num_rows();
num_cols = tsm->num_cols();
std::vector<Block>* row_blocks = crsm->mutable_row_blocks();
row_blocks->resize(num_rows);
for (int i = 0; i < row_blocks->size(); ++i) {
(*row_blocks)[i] = Block(1, i);
}
std::vector<Block>* col_blocks = crsm->mutable_col_blocks();
col_blocks->resize(num_cols);
for (int i = 0; i < col_blocks->size(); ++i) {
(*col_blocks)[i] = Block(1, i);
}
}
int num_rows;
int num_cols;
std::unique_ptr<TripletSparseMatrix> tsm;
std::unique_ptr<CompressedRowSparseMatrix> crsm;
};
TEST_F(CompressedRowSparseMatrixTest, Scale) {
Vector scale(num_cols);
for (int i = 0; i < num_cols; ++i) {
scale(i) = i + 1;
}
tsm->ScaleColumns(scale.data());
crsm->ScaleColumns(scale.data());
CompareMatrices(tsm.get(), crsm.get());
}
TEST_F(CompressedRowSparseMatrixTest, DeleteRows) {
// Clear the row and column blocks as these are purely scalar tests.
crsm->mutable_row_blocks()->clear();
crsm->mutable_col_blocks()->clear();
for (int i = 0; i < num_rows; ++i) {
tsm->Resize(num_rows - i, num_cols);
crsm->DeleteRows(crsm->num_rows() - tsm->num_rows());
CompareMatrices(tsm.get(), crsm.get());
}
}
TEST_F(CompressedRowSparseMatrixTest, AppendRows) {
// Clear the row and column blocks as these are purely scalar tests.
crsm->mutable_row_blocks()->clear();
crsm->mutable_col_blocks()->clear();
for (int i = 0; i < num_rows; ++i) {
TripletSparseMatrix tsm_appendage(*tsm);
tsm_appendage.Resize(i, num_cols);
tsm->AppendRows(tsm_appendage);
auto crsm_appendage =
CompressedRowSparseMatrix::FromTripletSparseMatrix(tsm_appendage);
crsm->AppendRows(*crsm_appendage);
CompareMatrices(tsm.get(), crsm.get());
}
}
TEST_F(CompressedRowSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) {
int num_diagonal_rows = crsm->num_cols();
auto diagonal = std::make_unique<double[]>(num_diagonal_rows);
for (int i = 0; i < num_diagonal_rows; ++i) {
diagonal[i] = i;
}
std::vector<Block> row_and_column_blocks;
row_and_column_blocks.emplace_back(1, 0);
row_and_column_blocks.emplace_back(2, 1);
row_and_column_blocks.emplace_back(2, 3);
const std::vector<Block> pre_row_blocks = crsm->row_blocks();
const std::vector<Block> pre_col_blocks = crsm->col_blocks();
auto appendage = CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
diagonal.get(), row_and_column_blocks);
crsm->AppendRows(*appendage);
const std::vector<Block> post_row_blocks = crsm->row_blocks();
const std::vector<Block> post_col_blocks = crsm->col_blocks();
std::vector<Block> expected_row_blocks = pre_row_blocks;
expected_row_blocks.insert(expected_row_blocks.end(),
row_and_column_blocks.begin(),
row_and_column_blocks.end());
std::vector<Block> expected_col_blocks = pre_col_blocks;
EXPECT_EQ(expected_row_blocks, crsm->row_blocks());
EXPECT_EQ(expected_col_blocks, crsm->col_blocks());
crsm->DeleteRows(num_diagonal_rows);
EXPECT_EQ(crsm->row_blocks(), pre_row_blocks);
EXPECT_EQ(crsm->col_blocks(), pre_col_blocks);
}
TEST_F(CompressedRowSparseMatrixTest, ToDenseMatrix) {
Matrix tsm_dense;
Matrix crsm_dense;
tsm->ToDenseMatrix(&tsm_dense);
crsm->ToDenseMatrix(&crsm_dense);
EXPECT_EQ((tsm_dense - crsm_dense).norm(), 0.0);
}
TEST_F(CompressedRowSparseMatrixTest, ToCRSMatrix) {
CRSMatrix crs_matrix;
crsm->ToCRSMatrix(&crs_matrix);
EXPECT_EQ(crsm->num_rows(), crs_matrix.num_rows);
EXPECT_EQ(crsm->num_cols(), crs_matrix.num_cols);
EXPECT_EQ(crsm->num_rows() + 1, crs_matrix.rows.size());
EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.cols.size());
EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.values.size());
for (int i = 0; i < crsm->num_rows() + 1; ++i) {
EXPECT_EQ(crsm->rows()[i], crs_matrix.rows[i]);
}
for (int i = 0; i < crsm->num_nonzeros(); ++i) {
EXPECT_EQ(crsm->cols()[i], crs_matrix.cols[i]);
EXPECT_EQ(crsm->values()[i], crs_matrix.values[i]);
}
}
TEST(CompressedRowSparseMatrix, CreateBlockDiagonalMatrix) {
std::vector<Block> blocks;
blocks.emplace_back(1, 0);
blocks.emplace_back(2, 1);
blocks.emplace_back(2, 3);
Vector diagonal(5);
for (int i = 0; i < 5; ++i) {
diagonal(i) = i + 1;
}
auto matrix = CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
diagonal.data(), blocks);
EXPECT_EQ(matrix->num_rows(), 5);
EXPECT_EQ(matrix->num_cols(), 5);
EXPECT_EQ(matrix->num_nonzeros(), 9);
EXPECT_EQ(blocks, matrix->row_blocks());
EXPECT_EQ(blocks, matrix->col_blocks());
Vector x(5);
Vector y(5);
x.setOnes();
y.setZero();
matrix->RightMultiplyAndAccumulate(x.data(), y.data());
for (int i = 0; i < diagonal.size(); ++i) {
EXPECT_EQ(y[i], diagonal[i]);
}
y.setZero();
matrix->LeftMultiplyAndAccumulate(x.data(), y.data());
for (int i = 0; i < diagonal.size(); ++i) {
EXPECT_EQ(y[i], diagonal[i]);
}
Matrix dense;
matrix->ToDenseMatrix(&dense);
EXPECT_EQ((dense.diagonal() - diagonal).norm(), 0.0);
}
TEST(CompressedRowSparseMatrix, Transpose) {
// 0 1 0 2 3 0
// 4 5 6 0 0 7
// 8 9 0 10 11 0
// 12 0 13 14 15 0
// 0 16 17 0 0 0
// Block structure:
// A A A A B B
// A A A A B B
// A A A A B B
// C C C C D D
// C C C C D D
// C C C C D D
CompressedRowSparseMatrix matrix(5, 6, 30);
int* rows = matrix.mutable_rows();
int* cols = matrix.mutable_cols();
double* values = matrix.mutable_values();
matrix.mutable_row_blocks()->emplace_back(3, 0);
matrix.mutable_row_blocks()->emplace_back(3, 3);
matrix.mutable_col_blocks()->emplace_back(4, 0);
matrix.mutable_col_blocks()->emplace_back(2, 4);
rows[0] = 0;
cols[0] = 1;
cols[1] = 3;
cols[2] = 4;
rows[1] = 3;
cols[3] = 0;
cols[4] = 1;
cols[5] = 2;
cols[6] = 5;
rows[2] = 7;
cols[7] = 0;
cols[8] = 1;
cols[9] = 3;
cols[10] = 4;
rows[3] = 11;
cols[11] = 0;
cols[12] = 2;
cols[13] = 3;
cols[14] = 4;
rows[4] = 15;
cols[15] = 1;
cols[16] = 2;
rows[5] = 17;
std::iota(values, values + 17, 1);
auto transpose = matrix.Transpose();
ASSERT_EQ(transpose->row_blocks().size(), matrix.col_blocks().size());
for (int i = 0; i < transpose->row_blocks().size(); ++i) {
EXPECT_EQ(transpose->row_blocks()[i], matrix.col_blocks()[i]);
}
ASSERT_EQ(transpose->col_blocks().size(), matrix.row_blocks().size());
for (int i = 0; i < transpose->col_blocks().size(); ++i) {
EXPECT_EQ(transpose->col_blocks()[i], matrix.row_blocks()[i]);
}
Matrix dense_matrix;
matrix.ToDenseMatrix(&dense_matrix);
Matrix dense_transpose;
transpose->ToDenseMatrix(&dense_transpose);
EXPECT_NEAR((dense_matrix - dense_transpose.transpose()).norm(), 0.0, 1e-14);
}
TEST(CompressedRowSparseMatrix, FromTripletSparseMatrix) {
std::mt19937 prng;
TripletSparseMatrix::RandomMatrixOptions options;
options.num_rows = 5;
options.num_cols = 7;
options.density = 0.5;
const int kNumTrials = 10;
for (int i = 0; i < kNumTrials; ++i) {
auto tsm = TripletSparseMatrix::CreateRandomMatrix(options, prng);
auto crsm = CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm);
Matrix expected;
tsm->ToDenseMatrix(&expected);
Matrix actual;
crsm->ToDenseMatrix(&actual);
EXPECT_NEAR((expected - actual).norm() / actual.norm(),
0.0,
std::numeric_limits<double>::epsilon())
<< "\nexpected: \n"
<< expected << "\nactual: \n"
<< actual;
}
}
TEST(CompressedRowSparseMatrix, FromTripletSparseMatrixTransposed) {
std::mt19937 prng;
TripletSparseMatrix::RandomMatrixOptions options;
options.num_rows = 5;
options.num_cols = 7;
options.density = 0.5;
const int kNumTrials = 10;
for (int i = 0; i < kNumTrials; ++i) {
auto tsm = TripletSparseMatrix::CreateRandomMatrix(options, prng);
auto crsm =
CompressedRowSparseMatrix::FromTripletSparseMatrixTransposed(*tsm);
Matrix tmp;
tsm->ToDenseMatrix(&tmp);
Matrix expected = tmp.transpose();
Matrix actual;
crsm->ToDenseMatrix(&actual);
EXPECT_NEAR((expected - actual).norm() / actual.norm(),
0.0,
std::numeric_limits<double>::epsilon())
<< "\nexpected: \n"
<< expected << "\nactual: \n"
<< actual;
}
}
using Param = ::testing::tuple<CompressedRowSparseMatrix::StorageType>;
static std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
if (::testing::get<0>(info.param) ==
CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) {
return "UPPER";
}
if (::testing::get<0>(info.param) ==
CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) {
return "LOWER";
}
return "UNSYMMETRIC";
}
class RightMultiplyAndAccumulateTest : public ::testing::TestWithParam<Param> {
};
TEST_P(RightMultiplyAndAccumulateTest, _) {
const int kMinNumBlocks = 1;
const int kMaxNumBlocks = 10;
const int kMinBlockSize = 1;
const int kMaxBlockSize = 5;
const int kNumTrials = 10;
std::mt19937 prng;
std::uniform_real_distribution<double> uniform(0.5, 1.0);
for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks;
++num_blocks) {
for (int trial = 0; trial < kNumTrials; ++trial) {
Param param = GetParam();
CompressedRowSparseMatrix::RandomMatrixOptions options;
options.num_col_blocks = num_blocks;
options.min_col_block_size = kMinBlockSize;
options.max_col_block_size = kMaxBlockSize;
options.num_row_blocks = 2 * num_blocks;
options.min_row_block_size = kMinBlockSize;
options.max_row_block_size = kMaxBlockSize;
options.block_density = uniform(prng);
options.storage_type = ::testing::get<0>(param);
auto matrix =
CompressedRowSparseMatrix::CreateRandomMatrix(options, prng);
const int num_rows = matrix->num_rows();
const int num_cols = matrix->num_cols();
Vector x(num_cols);
x.setRandom();
Vector actual_y(num_rows);
actual_y.setZero();
matrix->RightMultiplyAndAccumulate(x.data(), actual_y.data());
Matrix dense;
matrix->ToDenseMatrix(&dense);
Vector expected_y;
if (::testing::get<0>(param) ==
CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) {
expected_y = dense.selfadjointView<Eigen::Upper>() * x;
} else if (::testing::get<0>(param) ==
CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) {
expected_y = dense.selfadjointView<Eigen::Lower>() * x;
} else {
expected_y = dense * x;
}
ASSERT_NEAR((expected_y - actual_y).norm() / actual_y.norm(),
0.0,
std::numeric_limits<double>::epsilon() * 10)
<< "\n"
<< dense << "x:\n"
<< x.transpose() << "\n"
<< "expected: \n"
<< expected_y.transpose() << "\n"
<< "actual: \n"
<< actual_y.transpose();
}
}
}
INSTANTIATE_TEST_SUITE_P(
CompressedRowSparseMatrix,
RightMultiplyAndAccumulateTest,
::testing::Values(CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR,
CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR,
CompressedRowSparseMatrix::StorageType::UNSYMMETRIC),
ParamInfoToString);
class LeftMultiplyAndAccumulateTest : public ::testing::TestWithParam<Param> {};
TEST_P(LeftMultiplyAndAccumulateTest, _) {
const int kMinNumBlocks = 1;
const int kMaxNumBlocks = 10;
const int kMinBlockSize = 1;
const int kMaxBlockSize = 5;
const int kNumTrials = 10;
std::mt19937 prng;
std::uniform_real_distribution<double> uniform(0.5, 1.0);
for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks;
++num_blocks) {
for (int trial = 0; trial < kNumTrials; ++trial) {
Param param = GetParam();
CompressedRowSparseMatrix::RandomMatrixOptions options;
options.num_col_blocks = num_blocks;
options.min_col_block_size = kMinBlockSize;
options.max_col_block_size = kMaxBlockSize;
options.num_row_blocks = 2 * num_blocks;
options.min_row_block_size = kMinBlockSize;
options.max_row_block_size = kMaxBlockSize;
options.block_density = uniform(prng);
options.storage_type = ::testing::get<0>(param);
auto matrix =
CompressedRowSparseMatrix::CreateRandomMatrix(options, prng);
const int num_rows = matrix->num_rows();
const int num_cols = matrix->num_cols();
Vector x(num_rows);
x.setRandom();
Vector actual_y(num_cols);
actual_y.setZero();
matrix->LeftMultiplyAndAccumulate(x.data(), actual_y.data());
Matrix dense;
matrix->ToDenseMatrix(&dense);
Vector expected_y;
if (::testing::get<0>(param) ==
CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) {
expected_y = dense.selfadjointView<Eigen::Upper>() * x;
} else if (::testing::get<0>(param) ==
CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) {
expected_y = dense.selfadjointView<Eigen::Lower>() * x;
} else {
expected_y = dense.transpose() * x;
}
ASSERT_NEAR((expected_y - actual_y).norm() / actual_y.norm(),
0.0,
std::numeric_limits<double>::epsilon() * 10)
<< "\n"
<< dense << "x\n"
<< x.transpose() << "\n"
<< "expected: \n"
<< expected_y.transpose() << "\n"
<< "actual: \n"
<< actual_y.transpose();
}
}
}
INSTANTIATE_TEST_SUITE_P(
CompressedRowSparseMatrix,
LeftMultiplyAndAccumulateTest,
::testing::Values(CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR,
CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR,
CompressedRowSparseMatrix::StorageType::UNSYMMETRIC),
ParamInfoToString);
class SquaredColumnNormTest : public ::testing::TestWithParam<Param> {};
TEST_P(SquaredColumnNormTest, _) {
const int kMinNumBlocks = 1;
const int kMaxNumBlocks = 10;
const int kMinBlockSize = 1;
const int kMaxBlockSize = 5;
const int kNumTrials = 10;
std::mt19937 prng;
std::uniform_real_distribution<double> uniform(0.5, 1.0);
for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks;
++num_blocks) {
for (int trial = 0; trial < kNumTrials; ++trial) {
Param param = GetParam();
CompressedRowSparseMatrix::RandomMatrixOptions options;
options.num_col_blocks = num_blocks;
options.min_col_block_size = kMinBlockSize;
options.max_col_block_size = kMaxBlockSize;
options.num_row_blocks = 2 * num_blocks;
options.min_row_block_size = kMinBlockSize;
options.max_row_block_size = kMaxBlockSize;
options.block_density = uniform(prng);
options.storage_type = ::testing::get<0>(param);
auto matrix =
CompressedRowSparseMatrix::CreateRandomMatrix(options, prng);
const int num_cols = matrix->num_cols();
Vector actual(num_cols);
actual.setZero();
matrix->SquaredColumnNorm(actual.data());
Matrix dense;
matrix->ToDenseMatrix(&dense);
Vector expected;
if (::testing::get<0>(param) ==
CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR) {
const Matrix full = dense.selfadjointView<Eigen::Upper>();
expected = full.colwise().squaredNorm();
} else if (::testing::get<0>(param) ==
CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR) {
const Matrix full = dense.selfadjointView<Eigen::Lower>();
expected = full.colwise().squaredNorm();
} else {
expected = dense.colwise().squaredNorm();
}
ASSERT_NEAR((expected - actual).norm() / actual.norm(),
0.0,
std::numeric_limits<double>::epsilon() * 10)
<< "\n"
<< dense << "expected: \n"
<< expected.transpose() << "\n"
<< "actual: \n"
<< actual.transpose();
}
}
}
INSTANTIATE_TEST_SUITE_P(
CompressedRowSparseMatrix,
SquaredColumnNormTest,
::testing::Values(CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR,
CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR,
CompressedRowSparseMatrix::StorageType::UNSYMMETRIC),
ParamInfoToString);
const int kMaxNumThreads = 8;
class CompressedRowSparseMatrixParallelTest
: public ::testing::TestWithParam<int> {
void SetUp() final { context_.EnsureMinimumThreads(kMaxNumThreads); }
protected:
ContextImpl context_;
};
TEST_P(CompressedRowSparseMatrixParallelTest,
RightMultiplyAndAccumulateUnsymmetric) {
const int kMinNumBlocks = 1;
const int kMaxNumBlocks = 10;
const int kMinBlockSize = 1;
const int kMaxBlockSize = 5;
const int kNumTrials = 10;
const int kNumThreads = GetParam();
std::mt19937 prng;
std::uniform_real_distribution<double> uniform(0.5, 1.0);
for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks;
++num_blocks) {
for (int trial = 0; trial < kNumTrials; ++trial) {
CompressedRowSparseMatrix::RandomMatrixOptions options;
options.num_col_blocks = num_blocks;
options.min_col_block_size = kMinBlockSize;
options.max_col_block_size = kMaxBlockSize;
options.num_row_blocks = 2 * num_blocks;
options.min_row_block_size = kMinBlockSize;
options.max_row_block_size = kMaxBlockSize;
options.block_density = uniform(prng);
options.storage_type =
CompressedRowSparseMatrix::StorageType::UNSYMMETRIC;
auto matrix =
CompressedRowSparseMatrix::CreateRandomMatrix(options, prng);
const int num_rows = matrix->num_rows();
const int num_cols = matrix->num_cols();
Vector x(num_cols);
x.setRandom();
Vector actual_y(num_rows);
actual_y.setZero();
matrix->RightMultiplyAndAccumulate(
x.data(), actual_y.data(), &context_, kNumThreads);
Matrix dense;
matrix->ToDenseMatrix(&dense);
Vector expected_y = dense * x;
ASSERT_NEAR((expected_y - actual_y).norm() / actual_y.norm(),
0.0,
std::numeric_limits<double>::epsilon() * 10)
<< "\n"
<< dense << "x:\n"
<< x.transpose() << "\n"
<< "expected: \n"
<< expected_y.transpose() << "\n"
<< "actual: \n"
<< actual_y.transpose();
}
}
}
INSTANTIATE_TEST_SUITE_P(ParallelProducts,
CompressedRowSparseMatrixParallelTest,
::testing::Values(1, 2, 4, 8),
::testing::PrintToStringParamName());
// TODO(sameeragarwal) Add tests for the random matrix creation methods.
} // namespace ceres::internal
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