blob: 9422bf58b1e83e7d6ad375ca103d3cd58c8c3d06 [file] [log] [blame]
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 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/sparse_cholesky.h"
#include <memory>
#include <numeric>
#include <vector>
#include "Eigen/Dense"
#include "Eigen/SparseCore"
#include "ceres/block_sparse_matrix.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/inner_product_computer.h"
#include "ceres/internal/eigen.h"
#include "ceres/iterative_refiner.h"
#include "ceres/random.h"
#include "glog/logging.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
namespace {
std::unique_ptr<BlockSparseMatrix> CreateRandomFullRankMatrix(
const int num_col_blocks,
const int min_col_block_size,
const int max_col_block_size,
const double block_density) {
// Create a random matrix
BlockSparseMatrix::RandomMatrixOptions options;
options.num_col_blocks = num_col_blocks;
options.min_col_block_size = min_col_block_size;
options.max_col_block_size = max_col_block_size;
options.num_row_blocks = 2 * num_col_blocks;
options.min_row_block_size = 1;
options.max_row_block_size = max_col_block_size;
options.block_density = block_density;
auto random_matrix = BlockSparseMatrix::CreateRandomMatrix(options);
// Add a diagonal block sparse matrix to make it full rank.
Vector diagonal = Vector::Ones(random_matrix->num_cols());
auto block_diagonal = BlockSparseMatrix::CreateDiagonalMatrix(
diagonal.data(), random_matrix->block_structure()->cols);
random_matrix->AppendRows(*block_diagonal);
return random_matrix;
}
static bool ComputeExpectedSolution(const CompressedRowSparseMatrix& lhs,
const Vector& rhs,
Vector* solution) {
Matrix eigen_lhs;
lhs.ToDenseMatrix(&eigen_lhs);
if (lhs.storage_type() == CompressedRowSparseMatrix::UPPER_TRIANGULAR) {
Matrix full_lhs = eigen_lhs.selfadjointView<Eigen::Upper>();
Eigen::LLT<Matrix, Eigen::Upper> llt =
eigen_lhs.selfadjointView<Eigen::Upper>().llt();
if (llt.info() != Eigen::Success) {
return false;
}
*solution = llt.solve(rhs);
return (llt.info() == Eigen::Success);
}
Matrix full_lhs = eigen_lhs.selfadjointView<Eigen::Lower>();
Eigen::LLT<Matrix, Eigen::Lower> llt =
eigen_lhs.selfadjointView<Eigen::Lower>().llt();
if (llt.info() != Eigen::Success) {
return false;
}
*solution = llt.solve(rhs);
return (llt.info() == Eigen::Success);
}
void SparseCholeskySolverUnitTest(
const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,
const OrderingType ordering_type,
const bool use_block_structure,
const int num_blocks,
const int min_block_size,
const int max_block_size,
const double block_density) {
LinearSolver::Options sparse_cholesky_options;
sparse_cholesky_options.sparse_linear_algebra_library_type =
sparse_linear_algebra_library_type;
sparse_cholesky_options.use_postordering = (ordering_type == AMD);
auto sparse_cholesky = SparseCholesky::Create(sparse_cholesky_options);
const CompressedRowSparseMatrix::StorageType storage_type =
sparse_cholesky->StorageType();
auto m = CreateRandomFullRankMatrix(
num_blocks, min_block_size, max_block_size, block_density);
auto inner_product_computer = InnerProductComputer::Create(*m, storage_type);
inner_product_computer->Compute();
CompressedRowSparseMatrix* lhs = inner_product_computer->mutable_result();
if (!use_block_structure) {
lhs->mutable_row_blocks()->clear();
lhs->mutable_col_blocks()->clear();
}
Vector rhs = Vector::Random(lhs->num_rows());
Vector expected(lhs->num_rows());
Vector actual(lhs->num_rows());
EXPECT_TRUE(ComputeExpectedSolution(*lhs, rhs, &expected));
std::string message;
EXPECT_EQ(
sparse_cholesky->FactorAndSolve(lhs, rhs.data(), actual.data(), &message),
LINEAR_SOLVER_SUCCESS);
Matrix eigen_lhs;
lhs->ToDenseMatrix(&eigen_lhs);
EXPECT_NEAR((actual - expected).norm() / actual.norm(),
0.0,
std::numeric_limits<double>::epsilon() * 20)
<< "\n"
<< eigen_lhs;
}
using Param =
::testing::tuple<SparseLinearAlgebraLibraryType, OrderingType, bool>;
std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
Param param = info.param;
std::stringstream ss;
ss << SparseLinearAlgebraLibraryTypeToString(::testing::get<0>(param)) << "_"
<< (::testing::get<1>(param) == AMD ? "AMD" : "NATURAL") << "_"
<< (::testing::get<2>(param) ? "UseBlockStructure" : "NoBlockStructure");
return ss.str();
}
} // namespace
class SparseCholeskyTest : public ::testing::TestWithParam<Param> {};
TEST_P(SparseCholeskyTest, FactorAndSolve) {
SetRandomState(2982);
const int kMinNumBlocks = 1;
const int kMaxNumBlocks = 10;
const int kNumTrials = 10;
const int kMinBlockSize = 1;
const int kMaxBlockSize = 5;
for (int num_blocks = kMinNumBlocks; num_blocks < kMaxNumBlocks;
++num_blocks) {
for (int trial = 0; trial < kNumTrials; ++trial) {
const double block_density = std::max(0.1, RandDouble());
Param param = GetParam();
SparseCholeskySolverUnitTest(::testing::get<0>(param),
::testing::get<1>(param),
::testing::get<2>(param),
num_blocks,
kMinBlockSize,
kMaxBlockSize,
block_density);
}
}
}
namespace {
#ifndef CERES_NO_SUITESPARSE
INSTANTIATE_TEST_SUITE_P(SuiteSparseCholesky,
SparseCholeskyTest,
::testing::Combine(::testing::Values(SUITE_SPARSE),
::testing::Values(AMD, NATURAL),
::testing::Values(true, false)),
ParamInfoToString);
#endif
#ifndef CERES_NO_CXSPARSE
INSTANTIATE_TEST_SUITE_P(CXSparseCholesky,
SparseCholeskyTest,
::testing::Combine(::testing::Values(CX_SPARSE),
::testing::Values(AMD, NATURAL),
::testing::Values(true, false)),
ParamInfoToString);
#endif
#ifndef CERES_NO_ACCELERATE_SPARSE
INSTANTIATE_TEST_SUITE_P(
AccelerateSparseCholesky,
SparseCholeskyTest,
::testing::Combine(::testing::Values(ACCELERATE_SPARSE),
::testing::Values(AMD, NATURAL),
::testing::Values(true, false)),
ParamInfoToString);
INSTANTIATE_TEST_SUITE_P(
AccelerateSparseCholeskySingle,
SparseCholeskyTest,
::testing::Combine(::testing::Values(ACCELERATE_SPARSE),
::testing::Values(AMD, NATURAL),
::testing::Values(true, false)),
ParamInfoToString);
#endif
#ifdef CERES_USE_EIGEN_SPARSE
INSTANTIATE_TEST_SUITE_P(EigenSparseCholesky,
SparseCholeskyTest,
::testing::Combine(::testing::Values(EIGEN_SPARSE),
::testing::Values(AMD, NATURAL),
::testing::Values(true, false)),
ParamInfoToString);
INSTANTIATE_TEST_SUITE_P(EigenSparseCholeskySingle,
SparseCholeskyTest,
::testing::Combine(::testing::Values(EIGEN_SPARSE),
::testing::Values(AMD, NATURAL),
::testing::Values(true, false)),
ParamInfoToString);
#endif
class MockSparseCholesky : public SparseCholesky {
public:
MOCK_CONST_METHOD0(StorageType, CompressedRowSparseMatrix::StorageType());
MOCK_METHOD2(Factorize,
LinearSolverTerminationType(CompressedRowSparseMatrix* lhs,
std::string* message));
MOCK_METHOD3(Solve,
LinearSolverTerminationType(const double* rhs,
double* solution,
std::string* message));
};
class MockIterativeRefiner : public IterativeRefiner {
public:
MockIterativeRefiner() : IterativeRefiner(1) {}
MOCK_METHOD4(Refine,
void(const SparseMatrix& lhs,
const double* rhs,
SparseCholesky* sparse_cholesky,
double* solution));
};
using testing::_;
using testing::Return;
TEST(RefinedSparseCholesky, StorageType) {
auto* mock_sparse_cholesky = new MockSparseCholesky;
auto* mock_iterative_refiner = new MockIterativeRefiner;
EXPECT_CALL(*mock_sparse_cholesky, StorageType())
.Times(1)
.WillRepeatedly(Return(CompressedRowSparseMatrix::UPPER_TRIANGULAR));
EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0);
std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
std::unique_ptr<IterativeRefiner> iterative_refiner(mock_iterative_refiner);
RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
std::move(iterative_refiner));
EXPECT_EQ(refined_sparse_cholesky.StorageType(),
CompressedRowSparseMatrix::UPPER_TRIANGULAR);
};
TEST(RefinedSparseCholesky, Factorize) {
auto* mock_sparse_cholesky = new MockSparseCholesky;
auto* mock_iterative_refiner = new MockIterativeRefiner;
EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _))
.Times(1)
.WillRepeatedly(Return(LINEAR_SOLVER_SUCCESS));
EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0);
std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
std::unique_ptr<IterativeRefiner> iterative_refiner(mock_iterative_refiner);
RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
std::move(iterative_refiner));
CompressedRowSparseMatrix m(1, 1, 1);
std::string message;
EXPECT_EQ(refined_sparse_cholesky.Factorize(&m, &message),
LINEAR_SOLVER_SUCCESS);
};
TEST(RefinedSparseCholesky, FactorAndSolveWithUnsuccessfulFactorization) {
auto* mock_sparse_cholesky = new MockSparseCholesky;
auto* mock_iterative_refiner = new MockIterativeRefiner;
EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _))
.Times(1)
.WillRepeatedly(Return(LINEAR_SOLVER_FAILURE));
EXPECT_CALL(*mock_sparse_cholesky, Solve(_, _, _)).Times(0);
EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0);
std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
std::unique_ptr<IterativeRefiner> iterative_refiner(mock_iterative_refiner);
RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
std::move(iterative_refiner));
CompressedRowSparseMatrix m(1, 1, 1);
std::string message;
double rhs;
double solution;
EXPECT_EQ(
refined_sparse_cholesky.FactorAndSolve(&m, &rhs, &solution, &message),
LINEAR_SOLVER_FAILURE);
};
TEST(RefinedSparseCholesky, FactorAndSolveWithSuccess) {
auto* mock_sparse_cholesky = new MockSparseCholesky;
std::unique_ptr<MockIterativeRefiner> mock_iterative_refiner(
new MockIterativeRefiner);
EXPECT_CALL(*mock_sparse_cholesky, Factorize(_, _))
.Times(1)
.WillRepeatedly(Return(LINEAR_SOLVER_SUCCESS));
EXPECT_CALL(*mock_sparse_cholesky, Solve(_, _, _))
.Times(1)
.WillRepeatedly(Return(LINEAR_SOLVER_SUCCESS));
EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(1);
std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
std::unique_ptr<IterativeRefiner> iterative_refiner(
std::move(mock_iterative_refiner));
RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
std::move(iterative_refiner));
CompressedRowSparseMatrix m(1, 1, 1);
std::string message;
double rhs;
double solution;
EXPECT_EQ(
refined_sparse_cholesky.FactorAndSolve(&m, &rhs, &solution, &message),
LINEAR_SOLVER_SUCCESS);
};
} // namespace
} // namespace internal
} // namespace ceres