blob: ce016581cac4f43f6e8b5065eba30678690f46dd [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/eigensparse.h"
#include <memory>
#ifdef CERES_USE_EIGEN_SPARSE
#include <sstream>
#ifndef CERES_NO_EIGEN_METIS
#include <iostream> // This is needed because MetisSupport depends on iostream.
#include "Eigen/MetisSupport"
#endif
#include "Eigen/SparseCholesky"
#include "Eigen/SparseCore"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/linear_solver.h"
namespace ceres::internal {
// TODO(sameeragarwal): Use enable_if to clean up the implementations
// for when Scalar == double.
template <typename Solver>
class EigenSparseCholeskyTemplate final : public SparseCholesky {
public:
EigenSparseCholeskyTemplate() = default;
CompressedRowSparseMatrix::StorageType StorageType() const final {
return CompressedRowSparseMatrix::StorageType::LOWER_TRIANGULAR;
}
LinearSolverTerminationType Factorize(
const Eigen::SparseMatrix<typename Solver::Scalar>& lhs,
std::string* message) {
if (!analyzed_) {
solver_.analyzePattern(lhs);
if (VLOG_IS_ON(2)) {
std::stringstream ss;
solver_.dumpMemory(ss);
VLOG(2) << "Symbolic Analysis\n" << ss.str();
}
if (solver_.info() != Eigen::Success) {
*message = "Eigen failure. Unable to find symbolic factorization.";
return LinearSolverTerminationType::FATAL_ERROR;
}
analyzed_ = true;
}
solver_.factorize(lhs);
if (solver_.info() != Eigen::Success) {
*message = "Eigen failure. Unable to find numeric factorization.";
return LinearSolverTerminationType::FAILURE;
}
return LinearSolverTerminationType::SUCCESS;
}
LinearSolverTerminationType Solve(const double* rhs_ptr,
double* solution_ptr,
std::string* message) override {
CHECK(analyzed_) << "Solve called without a call to Factorize first.";
scalar_rhs_ = ConstVectorRef(rhs_ptr, solver_.cols())
.template cast<typename Solver::Scalar>();
// The two casts are needed if the Scalar in this class is not
// double. For code simplicity we are going to assume that Eigen
// is smart enough to figure out that casting a double Vector to a
// double Vector is a straight copy. If this turns into a
// performance bottleneck (unlikely), we can revisit this.
scalar_solution_ = solver_.solve(scalar_rhs_);
VectorRef(solution_ptr, solver_.cols()) =
scalar_solution_.template cast<double>();
if (solver_.info() != Eigen::Success) {
*message = "Eigen failure. Unable to do triangular solve.";
return LinearSolverTerminationType::FAILURE;
}
return LinearSolverTerminationType::SUCCESS;
}
LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs,
std::string* message) final {
CHECK_EQ(lhs->storage_type(), StorageType());
typename Solver::Scalar* values_ptr = nullptr;
if (std::is_same<typename Solver::Scalar, double>::value) {
values_ptr =
reinterpret_cast<typename Solver::Scalar*>(lhs->mutable_values());
} else {
// In the case where the scalar used in this class is not
// double. In that case, make a copy of the values array in the
// CompressedRowSparseMatrix and cast it to Scalar along the way.
values_ = ConstVectorRef(lhs->values(), lhs->num_nonzeros())
.cast<typename Solver::Scalar>();
values_ptr = values_.data();
}
Eigen::Map<Eigen::SparseMatrix<typename Solver::Scalar, Eigen::ColMajor>>
eigen_lhs(lhs->num_rows(),
lhs->num_rows(),
lhs->num_nonzeros(),
lhs->mutable_rows(),
lhs->mutable_cols(),
values_ptr);
return Factorize(eigen_lhs, message);
}
private:
Eigen::Matrix<typename Solver::Scalar, Eigen::Dynamic, 1> values_,
scalar_rhs_, scalar_solution_;
bool analyzed_{false};
Solver solver_;
};
std::unique_ptr<SparseCholesky> EigenSparseCholesky::Create(
const OrderingType ordering_type) {
using WithAMDOrdering = Eigen::SimplicialLDLT<Eigen::SparseMatrix<double>,
Eigen::Upper,
Eigen::AMDOrdering<int>>;
using WithNaturalOrdering =
Eigen::SimplicialLDLT<Eigen::SparseMatrix<double>,
Eigen::Upper,
Eigen::NaturalOrdering<int>>;
if (ordering_type == OrderingType::AMD) {
return std::make_unique<EigenSparseCholeskyTemplate<WithAMDOrdering>>();
} else if (ordering_type == OrderingType::NESDIS) {
#ifndef CERES_NO_EIGEN_METIS
using WithMetisOrdering = Eigen::SimplicialLDLT<Eigen::SparseMatrix<double>,
Eigen::Upper,
Eigen::MetisOrdering<int>>;
return std::make_unique<EigenSparseCholeskyTemplate<WithMetisOrdering>>();
#else
LOG(FATAL)
<< "Congratulations you have found a bug in Ceres Solver. Please "
"report it to the Ceres Solver developers.";
return nullptr;
#endif // CERES_NO_EIGEN_METIS
}
return std::make_unique<EigenSparseCholeskyTemplate<WithNaturalOrdering>>();
}
EigenSparseCholesky::~EigenSparseCholesky() = default;
std::unique_ptr<SparseCholesky> FloatEigenSparseCholesky::Create(
const OrderingType ordering_type) {
using WithAMDOrdering = Eigen::SimplicialLDLT<Eigen::SparseMatrix<float>,
Eigen::Upper,
Eigen::AMDOrdering<int>>;
using WithNaturalOrdering =
Eigen::SimplicialLDLT<Eigen::SparseMatrix<float>,
Eigen::Upper,
Eigen::NaturalOrdering<int>>;
if (ordering_type == OrderingType::AMD) {
return std::make_unique<EigenSparseCholeskyTemplate<WithAMDOrdering>>();
} else if (ordering_type == OrderingType::NESDIS) {
#ifndef CERES_NO_EIGEN_METIS
using WithMetisOrdering = Eigen::SimplicialLDLT<Eigen::SparseMatrix<float>,
Eigen::Upper,
Eigen::MetisOrdering<int>>;
return std::make_unique<EigenSparseCholeskyTemplate<WithMetisOrdering>>();
#else
LOG(FATAL)
<< "Congratulations you have found a bug in Ceres Solver. Please "
"report it to the Ceres Solver developers.";
return nullptr;
#endif // CERES_NO_EIGEN_METIS
}
return std::make_unique<EigenSparseCholeskyTemplate<WithNaturalOrdering>>();
}
FloatEigenSparseCholesky::~FloatEigenSparseCholesky() = default;
} // namespace ceres::internal
#endif // CERES_USE_EIGEN_SPARSE