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