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// 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"
#ifdef CERES_USE_EIGEN_SPARSE
#include <sstream>
#include "Eigen/SparseCholesky"
#include "Eigen/SparseCore"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/linear_solver.h"
namespace ceres {
namespace internal {
// TODO(sameeragarwal): Use enable_if to clean up the implementations
// for when Scalar == double.
template <typename Solver>
class EigenSparseCholeskyTemplate : public SparseCholesky {
public:
EigenSparseCholeskyTemplate() : analyzed_(false) {}
virtual ~EigenSparseCholeskyTemplate() {}
CompressedRowSparseMatrix::StorageType StorageType() const final {
return CompressedRowSparseMatrix::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 LINEAR_SOLVER_FATAL_ERROR;
}
analyzed_ = true;
}
solver_.factorize(lhs);
if (solver_.info() != Eigen::Success) {
*message = "Eigen failure. Unable to find numeric factorization.";
return LINEAR_SOLVER_FAILURE;
}
return LINEAR_SOLVER_SUCCESS;
}
LinearSolverTerminationType Solve(const double* rhs_ptr,
double* solution_ptr,
std::string* message) {
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 LINEAR_SOLVER_FAILURE;
}
return LINEAR_SOLVER_SUCCESS;
}
LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs,
std::string* message) final {
CHECK_EQ(lhs->storage_type(), StorageType());
typename Solver::Scalar* values_ptr = NULL;
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::MappedSparseMatrix<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_;
Solver solver_;
};
std::unique_ptr<SparseCholesky> EigenSparseCholesky::Create(
const OrderingType ordering_type) {
std::unique_ptr<SparseCholesky> sparse_cholesky;
typedef Eigen::SimplicialLDLT<Eigen::SparseMatrix<double>,
Eigen::Upper,
Eigen::AMDOrdering<int>>
WithAMDOrdering;
typedef Eigen::SimplicialLDLT<Eigen::SparseMatrix<double>,
Eigen::Upper,
Eigen::NaturalOrdering<int>>
WithNaturalOrdering;
if (ordering_type == AMD) {
sparse_cholesky.reset(new EigenSparseCholeskyTemplate<WithAMDOrdering>());
} else {
sparse_cholesky.reset(
new EigenSparseCholeskyTemplate<WithNaturalOrdering>());
}
return sparse_cholesky;
}
EigenSparseCholesky::~EigenSparseCholesky() {}
std::unique_ptr<SparseCholesky> FloatEigenSparseCholesky::Create(
const OrderingType ordering_type) {
std::unique_ptr<SparseCholesky> sparse_cholesky;
typedef Eigen::SimplicialLDLT<Eigen::SparseMatrix<float>,
Eigen::Upper,
Eigen::AMDOrdering<int>>
WithAMDOrdering;
typedef Eigen::SimplicialLDLT<Eigen::SparseMatrix<float>,
Eigen::Upper,
Eigen::NaturalOrdering<int>>
WithNaturalOrdering;
if (ordering_type == AMD) {
sparse_cholesky.reset(new EigenSparseCholeskyTemplate<WithAMDOrdering>());
} else {
sparse_cholesky.reset(
new EigenSparseCholeskyTemplate<WithNaturalOrdering>());
}
return sparse_cholesky;
}
FloatEigenSparseCholesky::~FloatEigenSparseCholesky() {}
} // namespace internal
} // namespace ceres
#endif // CERES_USE_EIGEN_SPARSE
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