| // 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 |
