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diff --git a/internal/ceres/eigensparse.cc b/internal/ceres/eigensparse.cc
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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() {}
+ virtual CompressedRowSparseMatrix::StorageType StorageType() const {
+ return CompressedRowSparseMatrix::LOWER_TRIANGULAR;
+ }
+
+ virtual 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;
+ }
+
+ virtual LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs,
+ std::string* message) {
+ 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;
+
+ // The preprocessor gymnastics here are dealing with the fact that
+ // before version 3.2.2, Eigen did not support a third template
+ // parameter to specify the ordering and it always defaults to AMD.
+#if EIGEN_VERSION_AT_LEAST(3, 2, 2)
+ 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>());
+ }
+#else
+ typedef Eigen::SimplicialLDLT<Eigen::SparseMatrix<double>, Eigen::Upper>
+ WithAMDOrdering;
+ sparse_cholesky.reset(new EigenSparseCholeskyTemplate<WithAMDOrdering>());
+#endif
+ return sparse_cholesky;
+}
+
+EigenSparseCholesky::~EigenSparseCholesky() {}
+
+std::unique_ptr<SparseCholesky> FloatEigenSparseCholesky::Create(
+ const OrderingType ordering_type) {
+ std::unique_ptr<SparseCholesky> sparse_cholesky;
+ // The preprocessor gymnastics here are dealing with the fact that
+ // before version 3.2.2, Eigen did not support a third template
+ // parameter to specify the ordering and it always defaults to AMD.
+#if EIGEN_VERSION_AT_LEAST(3, 2, 2)
+ 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>());
+ }
+#else
+ typedef Eigen::SimplicialLDLT<Eigen::SparseMatrix<float>, Eigen::Upper>
+ WithAMDOrdering;
+ sparse_cholesky.reset(new EigenSparseCholeskyTemplate<WithAMDOrdering>());
+#endif
+ return sparse_cholesky;
+}
+
+FloatEigenSparseCholesky::~FloatEigenSparseCholesky() {}
+
+} // namespace internal
+} // namespace ceres
+
+#endif // CERES_USE_EIGEN_SPARSE