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diff --git a/internal/ceres/sparse_normal_cholesky_solver.cc b/internal/ceres/sparse_normal_cholesky_solver.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// 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/sparse_normal_cholesky_solver.h"
+
+#include <algorithm>
+#include <cstring>
+#include <ctime>
+
+#ifndef CERES_NO_CXSPARSE
+#include "cs.h"
+#endif
+
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/linear_solver.h"
+#include "ceres/suitesparse.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/types.h"
+
+namespace ceres {
+namespace internal {
+
+SparseNormalCholeskySolver::SparseNormalCholeskySolver(
+    const LinearSolver::Options& options)
+    : options_(options) {
+#ifndef CERES_NO_SUITESPARSE
+  factor_ = NULL;
+#endif
+
+#ifndef CERES_NO_CXSPARSE
+  cxsparse_factor_ = NULL;
+#endif  // CERES_NO_CXSPARSE
+}
+
+SparseNormalCholeskySolver::~SparseNormalCholeskySolver() {
+#ifndef CERES_NO_SUITESPARSE
+  if (factor_ != NULL) {
+    ss_.Free(factor_);
+    factor_ = NULL;
+  }
+#endif
+
+#ifndef CERES_NO_CXSPARSE
+  if (cxsparse_factor_ != NULL) {
+    cxsparse_.Free(cxsparse_factor_);
+    cxsparse_factor_ = NULL;
+  }
+#endif  // CERES_NO_CXSPARSE
+}
+
+LinearSolver::Summary SparseNormalCholeskySolver::SolveImpl(
+    CompressedRowSparseMatrix* A,
+    const double* b,
+    const LinearSolver::PerSolveOptions& per_solve_options,
+    double * x) {
+  switch (options_.sparse_linear_algebra_library) {
+    case SUITE_SPARSE:
+      return SolveImplUsingSuiteSparse(A, b, per_solve_options, x);
+    case CX_SPARSE:
+      return SolveImplUsingCXSparse(A, b, per_solve_options, x);
+    default:
+      LOG(FATAL) << "Unknown sparse linear algebra library : "
+                 << options_.sparse_linear_algebra_library;
+  }
+
+  LOG(FATAL) << "Unknown sparse linear algebra library : "
+             << options_.sparse_linear_algebra_library;
+  return LinearSolver::Summary();
+}
+
+#ifndef CERES_NO_CXSPARSE
+LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
+    CompressedRowSparseMatrix* A,
+    const double* b,
+    const LinearSolver::PerSolveOptions& per_solve_options,
+    double * x) {
+  LinearSolver::Summary summary;
+  summary.num_iterations = 1;
+  const int num_cols = A->num_cols();
+  Vector Atb = Vector::Zero(num_cols);
+  A->LeftMultiply(b, Atb.data());
+
+  if (per_solve_options.D != NULL) {
+    // Temporarily append a diagonal block to the A matrix, but undo
+    // it before returning the matrix to the user.
+    CompressedRowSparseMatrix D(per_solve_options.D, num_cols);
+    A->AppendRows(D);
+  }
+
+  VectorRef(x, num_cols).setZero();
+
+  // Wrap the augmented Jacobian in a compressed sparse column matrix.
+  cs_di At = cxsparse_.CreateSparseMatrixTransposeView(A);
+
+  // Compute the normal equations. J'J delta = J'f and solve them
+  // using a sparse Cholesky factorization. Notice that when compared
+  // to SuiteSparse we have to explicitly compute the transpose of Jt,
+  // and then the normal equations before they can be
+  // factorized. CHOLMOD/SuiteSparse on the other hand can just work
+  // off of Jt to compute the Cholesky factorization of the normal
+  // equations.
+  cs_di* A2 = cs_transpose(&At, 1);
+  cs_di* AtA = cs_multiply(&At,A2);
+
+  cxsparse_.Free(A2);
+  if (per_solve_options.D != NULL) {
+    A->DeleteRows(num_cols);
+  }
+
+  // Compute symbolic factorization if not available.
+  if (cxsparse_factor_ == NULL) {
+    cxsparse_factor_ = CHECK_NOTNULL(cxsparse_.AnalyzeCholesky(AtA));
+  }
+
+  // Solve the linear system.
+  if (cxsparse_.SolveCholesky(AtA, cxsparse_factor_, Atb.data())) {
+    VectorRef(x, Atb.rows()) = Atb;
+    summary.termination_type = TOLERANCE;
+  }
+
+  cxsparse_.Free(AtA);
+  return summary;
+}
+#else
+LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
+    CompressedRowSparseMatrix* A,
+    const double* b,
+    const LinearSolver::PerSolveOptions& per_solve_options,
+    double * x) {
+  LOG(FATAL) << "No CXSparse support in Ceres.";
+
+  // Unreachable but MSVC does not know this.
+  return LinearSolver::Summary();
+}
+#endif
+
+#ifndef CERES_NO_SUITESPARSE
+LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
+    CompressedRowSparseMatrix* A,
+    const double* b,
+    const LinearSolver::PerSolveOptions& per_solve_options,
+    double * x) {
+  const time_t start_time = time(NULL);
+  const int num_cols = A->num_cols();
+
+  LinearSolver::Summary summary;
+  Vector Atb = Vector::Zero(num_cols);
+  A->LeftMultiply(b, Atb.data());
+
+  if (per_solve_options.D != NULL) {
+    // Temporarily append a diagonal block to the A matrix, but undo it before
+    // returning the matrix to the user.
+    CompressedRowSparseMatrix D(per_solve_options.D, num_cols);
+    A->AppendRows(D);
+  }
+
+  VectorRef(x, num_cols).setZero();
+
+  scoped_ptr<cholmod_sparse> lhs(ss_.CreateSparseMatrixTransposeView(A));
+  CHECK_NOTNULL(lhs.get());
+
+  cholmod_dense* rhs = ss_.CreateDenseVector(Atb.data(), num_cols, num_cols);
+  const time_t init_time = time(NULL);
+
+  if (factor_ == NULL) {
+    if (options_.use_block_amd) {
+      factor_ = ss_.BlockAnalyzeCholesky(lhs.get(),
+                                         A->col_blocks(),
+                                         A->row_blocks());
+    } else {
+      factor_ = ss_.AnalyzeCholesky(lhs.get());
+    }
+
+    if (VLOG_IS_ON(2)) {
+      cholmod_print_common("Symbolic Analysis", ss_.mutable_cc());
+    }
+  }
+
+  CHECK_NOTNULL(factor_);
+
+  const time_t symbolic_time = time(NULL);
+
+  cholmod_dense* sol = ss_.SolveCholesky(lhs.get(), factor_, rhs);
+  const time_t solve_time = time(NULL);
+
+  ss_.Free(rhs);
+  rhs = NULL;
+
+  if (per_solve_options.D != NULL) {
+    A->DeleteRows(num_cols);
+  }
+
+  summary.num_iterations = 1;
+  if (sol != NULL) {
+    memcpy(x, sol->x, num_cols * sizeof(*x));
+
+    ss_.Free(sol);
+    sol = NULL;
+    summary.termination_type = TOLERANCE;
+  }
+
+  const time_t cleanup_time = time(NULL);
+  VLOG(2) << "time (sec) total: " << (cleanup_time - start_time)
+          << " init: " << (init_time - start_time)
+          << " symbolic: " << (symbolic_time - init_time)
+          << " solve: " << (solve_time - symbolic_time)
+          << " cleanup: " << (cleanup_time - solve_time);
+  return summary;
+}
+#else
+LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
+    CompressedRowSparseMatrix* A,
+    const double* b,
+    const LinearSolver::PerSolveOptions& per_solve_options,
+    double * x) {
+  LOG(FATAL) << "No SuiteSparse support in Ceres.";
+
+  // Unreachable but MSVC does not know this.
+  return LinearSolver::Summary();
+}
+#endif
+
+}   // namespace internal
+}   // namespace ceres