internal/ceres/partitioned_matrix_view_test.cc - ceres-solver - Git at Google

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2022 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/partitioned_matrix_view.h"

 #include <memory>
 #include <random>
 #include <sstream>
 #include <string>
 #include <vector>

 #include "ceres/block_structure.h"
 #include "ceres/casts.h"
 #include "ceres/internal/eigen.h"
 #include "ceres/linear_least_squares_problems.h"
 #include "ceres/sparse_matrix.h"
 #include "glog/logging.h"
 #include "gtest/gtest.h"

 namespace ceres {
 namespace internal {

 const double kEpsilon = 1e-14;

 // Param = <problem_id, num_threads>
 using Param = ::testing::tuple<int, int>;

 static std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
   Param param = info.param;
   std::stringstream ss;
   ss << ::testing::get<0>(param) << "_" << ::testing::get<1>(param);
   return ss.str();
 }

 class PartitionedMatrixViewTest : public ::testing::TestWithParam<Param> {
  protected:
   void SetUp() final {
     const int problem_id = ::testing::get<0>(GetParam());
     const int num_threads = ::testing::get<1>(GetParam());
     auto problem = CreateLinearLeastSquaresProblemFromId(problem_id);
     CHECK(problem != nullptr);
     A_ = std::move(problem->A);
     auto block_sparse = down_cast<BlockSparseMatrix*>(A_.get());

     options_.num_threads = num_threads;
     options_.context = &context_;
     options_.elimination_groups.push_back(problem->num_eliminate_blocks);
     pmv_ = PartitionedMatrixViewBase::Create(options_, *block_sparse);

     LinearSolver::Options options_single_threaded = options_;
     options_single_threaded.num_threads = 1;
     pmv_single_threaded_ =
         PartitionedMatrixViewBase::Create(options_, *block_sparse);

     EXPECT_EQ(pmv_->num_col_blocks_e(), problem->num_eliminate_blocks);
     EXPECT_EQ(pmv_->num_col_blocks_f(),
               block_sparse->block_structure()->cols.size() -
                   problem->num_eliminate_blocks);
     EXPECT_EQ(pmv_->num_cols(), A_->num_cols());
     EXPECT_EQ(pmv_->num_rows(), A_->num_rows());
   }

   double RandDouble() { return distribution_(prng_); }

   LinearSolver::Options options_;
   ContextImpl context_;
   std::unique_ptr<LinearLeastSquaresProblem> problem_;
   std::unique_ptr<SparseMatrix> A_;
   std::unique_ptr<PartitionedMatrixViewBase> pmv_;
   std::unique_ptr<PartitionedMatrixViewBase> pmv_single_threaded_;
   std::mt19937 prng_;
   std::uniform_real_distribution<double> distribution_ =
       std::uniform_real_distribution<double>(0.0, 1.0);
 };

 TEST_P(PartitionedMatrixViewTest, RightMultiplyAndAccumulateE) {
   Vector x1(pmv_->num_cols_e());
   Vector x2(pmv_->num_cols());
   x2.setZero();

   for (int i = 0; i < pmv_->num_cols_e(); ++i) {
     x1(i) = x2(i) = RandDouble();
   }

   Vector expected = Vector::Zero(pmv_->num_rows());
   A_->RightMultiplyAndAccumulate(x2.data(), expected.data());

   Vector actual = Vector::Zero(pmv_->num_rows());
   pmv_->RightMultiplyAndAccumulateE(x1.data(), actual.data());

   for (int i = 0; i < pmv_->num_rows(); ++i) {
     EXPECT_NEAR(actual(i), expected(i), kEpsilon);
   }
 }

 TEST_P(PartitionedMatrixViewTest, RightMultiplyAndAccumulateF) {
   Vector x1(pmv_->num_cols_f());
   Vector x2(pmv_->num_cols());
   x2.setZero();

   for (int i = 0; i < pmv_->num_cols_f(); ++i) {
     x1(i) = x2(i + pmv_->num_cols_e()) = RandDouble();
   }

   Vector actual = Vector::Zero(pmv_->num_rows());
   pmv_->RightMultiplyAndAccumulateF(x1.data(), actual.data());

   Vector expected = Vector::Zero(pmv_->num_rows());
   A_->RightMultiplyAndAccumulate(x2.data(), expected.data());

   for (int i = 0; i < pmv_->num_rows(); ++i) {
     EXPECT_NEAR(actual(i), expected(i), kEpsilon);
   }
 }

 TEST_P(PartitionedMatrixViewTest, LeftMultiplyAndAccumulate) {
   Vector x = Vector::Zero(pmv_->num_rows());
   for (int i = 0; i < pmv_->num_rows(); ++i) {
     x(i) = RandDouble();
   }
   Vector x_pre = x;

   Vector expected = Vector::Zero(pmv_->num_cols());
   Vector e_actual = Vector::Zero(pmv_->num_cols_e());
   Vector f_actual = Vector::Zero(pmv_->num_cols_f());

   A_->LeftMultiplyAndAccumulate(x.data(), expected.data());
   pmv_->LeftMultiplyAndAccumulateE(x.data(), e_actual.data());
   pmv_->LeftMultiplyAndAccumulateF(x.data(), f_actual.data());

   for (int i = 0; i < pmv_->num_cols(); ++i) {
     EXPECT_NEAR(expected(i),
                 (i < pmv_->num_cols_e()) ? e_actual(i)
                                          : f_actual(i - pmv_->num_cols_e()),
                 kEpsilon);
   }
 }

 TEST_P(PartitionedMatrixViewTest, BlockDiagonalFtF) {
   std::unique_ptr<BlockSparseMatrix> block_diagonal_ff(
       pmv_->CreateBlockDiagonalFtF());
   const auto bs_diagonal = block_diagonal_ff->block_structure();
   const int num_rows = pmv_->num_rows();
   const int num_cols_f = pmv_->num_cols_f();
   const int num_cols_e = pmv_->num_cols_e();
   const int num_col_blocks_f = pmv_->num_col_blocks_f();
   const int num_col_blocks_e = pmv_->num_col_blocks_e();

   CHECK_EQ(block_diagonal_ff->num_rows(), num_cols_f);
   CHECK_EQ(block_diagonal_ff->num_cols(), num_cols_f);

   EXPECT_EQ(bs_diagonal->cols.size(), num_col_blocks_f);
   EXPECT_EQ(bs_diagonal->rows.size(), num_col_blocks_f);

   Matrix EF;
   A_->ToDenseMatrix(&EF);
   const auto F = EF.topRightCorner(num_rows, num_cols_f);

   Matrix expected_FtF = F.transpose() * F;
   Matrix actual_FtF;
   block_diagonal_ff->ToDenseMatrix(&actual_FtF);

   // FtF might be not block-diagonal
   auto bs = down_cast<BlockSparseMatrix*>(A_.get())->block_structure();
   for (int i = 0; i < num_col_blocks_f; ++i) {
     const auto col_block_f = bs->cols[num_col_blocks_e + i];
     const int block_size = col_block_f.size;
     const int block_pos = col_block_f.position - num_cols_e;
     const auto cell_expected =
         expected_FtF.block(block_pos, block_pos, block_size, block_size);
     auto cell_actual =
         actual_FtF.block(block_pos, block_pos, block_size, block_size);
     cell_actual -= cell_expected;
     EXPECT_NEAR(cell_actual.norm(), 0., kEpsilon);
   }
   // There should be nothing remaining outside block-diagonal
   EXPECT_NEAR(actual_FtF.norm(), 0., kEpsilon);
 }

 TEST_P(PartitionedMatrixViewTest, BlockDiagonalEtE) {
   std::unique_ptr<BlockSparseMatrix> block_diagonal_ee(
       pmv_->CreateBlockDiagonalEtE());
   const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure();
   const int num_rows = pmv_->num_rows();
   const int num_cols_e = pmv_->num_cols_e();
   const int num_col_blocks_e = pmv_->num_col_blocks_e();

   CHECK_EQ(block_diagonal_ee->num_rows(), num_cols_e);
   CHECK_EQ(block_diagonal_ee->num_cols(), num_cols_e);

   EXPECT_EQ(bs->cols.size(), num_col_blocks_e);
   EXPECT_EQ(bs->rows.size(), num_col_blocks_e);

   Matrix EF;
   A_->ToDenseMatrix(&EF);
   const auto E = EF.topLeftCorner(num_rows, num_cols_e);

   Matrix expected_EtE = E.transpose() * E;
   Matrix actual_EtE;
   block_diagonal_ee->ToDenseMatrix(&actual_EtE);

   EXPECT_NEAR((expected_EtE - actual_EtE).norm(), 0., kEpsilon);
 }

 TEST_P(PartitionedMatrixViewTest, UpdateBlockDiagonalEtE) {
   std::unique_ptr<BlockSparseMatrix> block_diagonal_ete(
       pmv_->CreateBlockDiagonalEtE());
   const int num_cols = pmv_->num_cols_e();

   Matrix multi_threaded(num_cols, num_cols);
   pmv_->UpdateBlockDiagonalEtE(block_diagonal_ete.get());
   block_diagonal_ete->ToDenseMatrix(&multi_threaded);

   Matrix single_threaded(num_cols, num_cols);
   pmv_single_threaded_->UpdateBlockDiagonalEtE(block_diagonal_ete.get());
   block_diagonal_ete->ToDenseMatrix(&single_threaded);

   EXPECT_NEAR((multi_threaded - single_threaded).norm(), 0., kEpsilon);
 }

 TEST_P(PartitionedMatrixViewTest, UpdateBlockDiagonalFtF) {
   std::unique_ptr<BlockSparseMatrix> block_diagonal_ftf(
       pmv_->CreateBlockDiagonalFtF());
   const int num_cols = pmv_->num_cols_f();

   Matrix multi_threaded(num_cols, num_cols);
   pmv_->UpdateBlockDiagonalFtF(block_diagonal_ftf.get());
   block_diagonal_ftf->ToDenseMatrix(&multi_threaded);

   Matrix single_threaded(num_cols, num_cols);
   pmv_single_threaded_->UpdateBlockDiagonalFtF(block_diagonal_ftf.get());
   block_diagonal_ftf->ToDenseMatrix(&single_threaded);

   EXPECT_NEAR((multi_threaded - single_threaded).norm(), 0., kEpsilon);
 }

 INSTANTIATE_TEST_SUITE_P(
     ParallelProducts,
     PartitionedMatrixViewTest,
     ::testing::Combine(::testing::Values(2, 4, 6),
                        ::testing::Values(1, 2, 3, 4, 5, 6, 7, 8)),
     ParamInfoToString);

 }  // namespace internal
 }  // namespace ceres
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2022 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/partitioned_matrix_view.h"

	#include <memory>
	#include <random>
	#include <sstream>
	#include <string>
	#include <vector>

	#include "ceres/block_structure.h"
	#include "ceres/casts.h"
	#include "ceres/internal/eigen.h"
	#include "ceres/linear_least_squares_problems.h"
	#include "ceres/sparse_matrix.h"
	#include "glog/logging.h"
	#include "gtest/gtest.h"

	namespace ceres {
	namespace internal {

	const double kEpsilon = 1e-14;

	// Param = <problem_id, num_threads>
	using Param = ::testing::tuple<int, int>;

	static std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
	Param param = info.param;
	std::stringstream ss;
	ss << ::testing::get<0>(param) << "_" << ::testing::get<1>(param);
	return ss.str();
	}

	class PartitionedMatrixViewTest : public ::testing::TestWithParam<Param> {
	protected:
	void SetUp() final {
	const int problem_id = ::testing::get<0>(GetParam());
	const int num_threads = ::testing::get<1>(GetParam());
	auto problem = CreateLinearLeastSquaresProblemFromId(problem_id);
	CHECK(problem != nullptr);
	A_ = std::move(problem->A);
	auto block_sparse = down_cast<BlockSparseMatrix*>(A_.get());

	options_.num_threads = num_threads;
	options_.context = &context_;
	options_.elimination_groups.push_back(problem->num_eliminate_blocks);
	pmv_ = PartitionedMatrixViewBase::Create(options_, *block_sparse);

	LinearSolver::Options options_single_threaded = options_;
	options_single_threaded.num_threads = 1;
	pmv_single_threaded_ =
	PartitionedMatrixViewBase::Create(options_, *block_sparse);

	EXPECT_EQ(pmv_->num_col_blocks_e(), problem->num_eliminate_blocks);
	EXPECT_EQ(pmv_->num_col_blocks_f(),
	block_sparse->block_structure()->cols.size() -
	problem->num_eliminate_blocks);
	EXPECT_EQ(pmv_->num_cols(), A_->num_cols());
	EXPECT_EQ(pmv_->num_rows(), A_->num_rows());
	}

	double RandDouble() { return distribution_(prng_); }

	LinearSolver::Options options_;
	ContextImpl context_;
	std::unique_ptr<LinearLeastSquaresProblem> problem_;
	std::unique_ptr<SparseMatrix> A_;
	std::unique_ptr<PartitionedMatrixViewBase> pmv_;
	std::unique_ptr<PartitionedMatrixViewBase> pmv_single_threaded_;
	std::mt19937 prng_;
	std::uniform_real_distribution<double> distribution_ =
	std::uniform_real_distribution<double>(0.0, 1.0);
	};

	TEST_P(PartitionedMatrixViewTest, RightMultiplyAndAccumulateE) {
	Vector x1(pmv_->num_cols_e());
	Vector x2(pmv_->num_cols());
	x2.setZero();

	for (int i = 0; i < pmv_->num_cols_e(); ++i) {
	x1(i) = x2(i) = RandDouble();
	}

	Vector expected = Vector::Zero(pmv_->num_rows());
	A_->RightMultiplyAndAccumulate(x2.data(), expected.data());

	Vector actual = Vector::Zero(pmv_->num_rows());
	pmv_->RightMultiplyAndAccumulateE(x1.data(), actual.data());

	for (int i = 0; i < pmv_->num_rows(); ++i) {
	EXPECT_NEAR(actual(i), expected(i), kEpsilon);
	}
	}

	TEST_P(PartitionedMatrixViewTest, RightMultiplyAndAccumulateF) {
	Vector x1(pmv_->num_cols_f());
	Vector x2(pmv_->num_cols());
	x2.setZero();

	for (int i = 0; i < pmv_->num_cols_f(); ++i) {
	x1(i) = x2(i + pmv_->num_cols_e()) = RandDouble();
	}

	Vector actual = Vector::Zero(pmv_->num_rows());
	pmv_->RightMultiplyAndAccumulateF(x1.data(), actual.data());

	Vector expected = Vector::Zero(pmv_->num_rows());
	A_->RightMultiplyAndAccumulate(x2.data(), expected.data());

	for (int i = 0; i < pmv_->num_rows(); ++i) {
	EXPECT_NEAR(actual(i), expected(i), kEpsilon);
	}
	}

	TEST_P(PartitionedMatrixViewTest, LeftMultiplyAndAccumulate) {
	Vector x = Vector::Zero(pmv_->num_rows());
	for (int i = 0; i < pmv_->num_rows(); ++i) {
	x(i) = RandDouble();
	}
	Vector x_pre = x;

	Vector expected = Vector::Zero(pmv_->num_cols());
	Vector e_actual = Vector::Zero(pmv_->num_cols_e());
	Vector f_actual = Vector::Zero(pmv_->num_cols_f());

	A_->LeftMultiplyAndAccumulate(x.data(), expected.data());
	pmv_->LeftMultiplyAndAccumulateE(x.data(), e_actual.data());
	pmv_->LeftMultiplyAndAccumulateF(x.data(), f_actual.data());

	for (int i = 0; i < pmv_->num_cols(); ++i) {
	EXPECT_NEAR(expected(i),
	(i < pmv_->num_cols_e()) ? e_actual(i)
	: f_actual(i - pmv_->num_cols_e()),
	kEpsilon);
	}
	}

	TEST_P(PartitionedMatrixViewTest, BlockDiagonalFtF) {
	std::unique_ptr<BlockSparseMatrix> block_diagonal_ff(
	pmv_->CreateBlockDiagonalFtF());
	const auto bs_diagonal = block_diagonal_ff->block_structure();
	const int num_rows = pmv_->num_rows();
	const int num_cols_f = pmv_->num_cols_f();
	const int num_cols_e = pmv_->num_cols_e();
	const int num_col_blocks_f = pmv_->num_col_blocks_f();
	const int num_col_blocks_e = pmv_->num_col_blocks_e();

	CHECK_EQ(block_diagonal_ff->num_rows(), num_cols_f);
	CHECK_EQ(block_diagonal_ff->num_cols(), num_cols_f);

	EXPECT_EQ(bs_diagonal->cols.size(), num_col_blocks_f);
	EXPECT_EQ(bs_diagonal->rows.size(), num_col_blocks_f);

	Matrix EF;
	A_->ToDenseMatrix(&EF);
	const auto F = EF.topRightCorner(num_rows, num_cols_f);

	Matrix expected_FtF = F.transpose() * F;
	Matrix actual_FtF;
	block_diagonal_ff->ToDenseMatrix(&actual_FtF);

	// FtF might be not block-diagonal
	auto bs = down_cast<BlockSparseMatrix*>(A_.get())->block_structure();
	for (int i = 0; i < num_col_blocks_f; ++i) {
	const auto col_block_f = bs->cols[num_col_blocks_e + i];
	const int block_size = col_block_f.size;
	const int block_pos = col_block_f.position - num_cols_e;
	const auto cell_expected =
	expected_FtF.block(block_pos, block_pos, block_size, block_size);
	auto cell_actual =
	actual_FtF.block(block_pos, block_pos, block_size, block_size);
	cell_actual -= cell_expected;
	EXPECT_NEAR(cell_actual.norm(), 0., kEpsilon);
	}
	// There should be nothing remaining outside block-diagonal
	EXPECT_NEAR(actual_FtF.norm(), 0., kEpsilon);
	}

	TEST_P(PartitionedMatrixViewTest, BlockDiagonalEtE) {
	std::unique_ptr<BlockSparseMatrix> block_diagonal_ee(
	pmv_->CreateBlockDiagonalEtE());
	const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure();
	const int num_rows = pmv_->num_rows();
	const int num_cols_e = pmv_->num_cols_e();
	const int num_col_blocks_e = pmv_->num_col_blocks_e();

	CHECK_EQ(block_diagonal_ee->num_rows(), num_cols_e);
	CHECK_EQ(block_diagonal_ee->num_cols(), num_cols_e);

	EXPECT_EQ(bs->cols.size(), num_col_blocks_e);
	EXPECT_EQ(bs->rows.size(), num_col_blocks_e);

	Matrix EF;
	A_->ToDenseMatrix(&EF);
	const auto E = EF.topLeftCorner(num_rows, num_cols_e);

	Matrix expected_EtE = E.transpose() * E;
	Matrix actual_EtE;
	block_diagonal_ee->ToDenseMatrix(&actual_EtE);

	EXPECT_NEAR((expected_EtE - actual_EtE).norm(), 0., kEpsilon);
	}

	TEST_P(PartitionedMatrixViewTest, UpdateBlockDiagonalEtE) {
	std::unique_ptr<BlockSparseMatrix> block_diagonal_ete(
	pmv_->CreateBlockDiagonalEtE());
	const int num_cols = pmv_->num_cols_e();

	Matrix multi_threaded(num_cols, num_cols);
	pmv_->UpdateBlockDiagonalEtE(block_diagonal_ete.get());
	block_diagonal_ete->ToDenseMatrix(&multi_threaded);

	Matrix single_threaded(num_cols, num_cols);
	pmv_single_threaded_->UpdateBlockDiagonalEtE(block_diagonal_ete.get());
	block_diagonal_ete->ToDenseMatrix(&single_threaded);

	EXPECT_NEAR((multi_threaded - single_threaded).norm(), 0., kEpsilon);
	}

	TEST_P(PartitionedMatrixViewTest, UpdateBlockDiagonalFtF) {
	std::unique_ptr<BlockSparseMatrix> block_diagonal_ftf(
	pmv_->CreateBlockDiagonalFtF());
	const int num_cols = pmv_->num_cols_f();

	Matrix multi_threaded(num_cols, num_cols);
	pmv_->UpdateBlockDiagonalFtF(block_diagonal_ftf.get());
	block_diagonal_ftf->ToDenseMatrix(&multi_threaded);

	Matrix single_threaded(num_cols, num_cols);
	pmv_single_threaded_->UpdateBlockDiagonalFtF(block_diagonal_ftf.get());
	block_diagonal_ftf->ToDenseMatrix(&single_threaded);

	EXPECT_NEAR((multi_threaded - single_threaded).norm(), 0., kEpsilon);
	}

	INSTANTIATE_TEST_SUITE_P(
	ParallelProducts,
	PartitionedMatrixViewTest,
	::testing::Combine(::testing::Values(2, 4, 6),
	::testing::Values(1, 2, 3, 4, 5, 6, 7, 8)),
	ParamInfoToString);

	} // namespace internal
	} // namespace ceres