diff --git a/internal/ceres/suitesparse.cc b/internal/ceres/suitesparse.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2015 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)
+
+// This include must come before any #ifndef check on Ceres compile options.
+#include "ceres/internal/port.h"
+
+#ifndef CERES_NO_SUITESPARSE
+#include "ceres/suitesparse.h"
+
+#include <vector>
+
+#include "ceres/compressed_col_sparse_matrix_utils.h"
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/linear_solver.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "cholmod.h"
+
+namespace ceres {
+namespace internal {
+
+using std::string;
+using std::vector;
+
+SuiteSparse::SuiteSparse() { cholmod_start(&cc_); }
+
+SuiteSparse::~SuiteSparse() { cholmod_finish(&cc_); }
+
+cholmod_sparse* SuiteSparse::CreateSparseMatrix(TripletSparseMatrix* A) {
+  cholmod_triplet triplet;
+
+  triplet.nrow = A->num_rows();
+  triplet.ncol = A->num_cols();
+  triplet.nzmax = A->max_num_nonzeros();
+  triplet.nnz = A->num_nonzeros();
+  triplet.i = reinterpret_cast<void*>(A->mutable_rows());
+  triplet.j = reinterpret_cast<void*>(A->mutable_cols());
+  triplet.x = reinterpret_cast<void*>(A->mutable_values());
+  triplet.stype = 0;  // Matrix is not symmetric.
+  triplet.itype = CHOLMOD_INT;
+  triplet.xtype = CHOLMOD_REAL;
+  triplet.dtype = CHOLMOD_DOUBLE;
+
+  return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
+}
+
+cholmod_sparse* SuiteSparse::CreateSparseMatrixTranspose(
+    TripletSparseMatrix* A) {
+  cholmod_triplet triplet;
+
+  triplet.ncol = A->num_rows();  // swap row and columns
+  triplet.nrow = A->num_cols();
+  triplet.nzmax = A->max_num_nonzeros();
+  triplet.nnz = A->num_nonzeros();
+
+  // swap rows and columns
+  triplet.j = reinterpret_cast<void*>(A->mutable_rows());
+  triplet.i = reinterpret_cast<void*>(A->mutable_cols());
+  triplet.x = reinterpret_cast<void*>(A->mutable_values());
+  triplet.stype = 0;  // Matrix is not symmetric.
+  triplet.itype = CHOLMOD_INT;
+  triplet.xtype = CHOLMOD_REAL;
+  triplet.dtype = CHOLMOD_DOUBLE;
+
+  return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
+}
+
+cholmod_sparse SuiteSparse::CreateSparseMatrixTransposeView(
+    CompressedRowSparseMatrix* A) {
+  cholmod_sparse m;
+  m.nrow = A->num_cols();
+  m.ncol = A->num_rows();
+  m.nzmax = A->num_nonzeros();
+  m.nz = nullptr;
+  m.p = reinterpret_cast<void*>(A->mutable_rows());
+  m.i = reinterpret_cast<void*>(A->mutable_cols());
+  m.x = reinterpret_cast<void*>(A->mutable_values());
+  m.z = nullptr;
+
+  if (A->storage_type() == CompressedRowSparseMatrix::LOWER_TRIANGULAR) {
+    m.stype = 1;
+  } else if (A->storage_type() == CompressedRowSparseMatrix::UPPER_TRIANGULAR) {
+    m.stype = -1;
+  } else {
+    m.stype = 0;
+  }
+
+  m.itype = CHOLMOD_INT;
+  m.xtype = CHOLMOD_REAL;
+  m.dtype = CHOLMOD_DOUBLE;
+  m.sorted = 1;
+  m.packed = 1;
+
+  return m;
+}
+
+cholmod_dense SuiteSparse::CreateDenseVectorView(const double* x, int size) {
+  cholmod_dense v;
+  v.nrow = size;
+  v.ncol = 1;
+  v.nzmax = size;
+  v.d = size;
+  v.x = const_cast<void*>(reinterpret_cast<const void*>(x));
+  v.xtype = CHOLMOD_REAL;
+  v.dtype = CHOLMOD_DOUBLE;
+  return v;
+}
+
+cholmod_dense* SuiteSparse::CreateDenseVector(const double* x,
+                                              int in_size,
+                                              int out_size) {
+  CHECK_LE(in_size, out_size);
+  cholmod_dense* v = cholmod_zeros(out_size, 1, CHOLMOD_REAL, &cc_);
+  if (x != nullptr) {
+    memcpy(v->x, x, in_size * sizeof(*x));
+  }
+  return v;
+}
+
+cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A,
+                                             string* message) {
+  // Cholmod can try multiple re-ordering strategies to find a fill
+  // reducing ordering. Here we just tell it use AMD with automatic
+  // matrix dependence choice of supernodal versus simplicial
+  // factorization.
+  cc_.nmethods = 1;
+  cc_.method[0].ordering = CHOLMOD_AMD;
+  cc_.supernodal = CHOLMOD_AUTO;
+
+  cholmod_factor* factor = cholmod_analyze(A, &cc_);
+  if (VLOG_IS_ON(2)) {
+    cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
+  }
+
+  if (cc_.status != CHOLMOD_OK) {
+    *message =
+        StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
+    return nullptr;
+  }
+
+  CHECK(factor != nullptr);
+  return factor;
+}
+
+cholmod_factor* SuiteSparse::BlockAnalyzeCholesky(cholmod_sparse* A,
+                                                  const vector<int>& row_blocks,
+                                                  const vector<int>& col_blocks,
+                                                  string* message) {
+  vector<int> ordering;
+  if (!BlockAMDOrdering(A, row_blocks, col_blocks, &ordering)) {
+    return nullptr;
+  }
+  return AnalyzeCholeskyWithUserOrdering(A, ordering, message);
+}
+
+cholmod_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
+    cholmod_sparse* A, const vector<int>& ordering, string* message) {
+  CHECK_EQ(ordering.size(), A->nrow);
+
+  cc_.nmethods = 1;
+  cc_.method[0].ordering = CHOLMOD_GIVEN;
+
+  cholmod_factor* factor =
+      cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), nullptr, 0, &cc_);
+  if (VLOG_IS_ON(2)) {
+    cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
+  }
+  if (cc_.status != CHOLMOD_OK) {
+    *message =
+        StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
+    return nullptr;
+  }
+
+  CHECK(factor != nullptr);
+  return factor;
+}
+
+cholmod_factor* SuiteSparse::AnalyzeCholeskyWithNaturalOrdering(
+    cholmod_sparse* A, string* message) {
+  cc_.nmethods = 1;
+  cc_.method[0].ordering = CHOLMOD_NATURAL;
+  cc_.postorder = 0;
+
+  cholmod_factor* factor = cholmod_analyze(A, &cc_);
+  if (VLOG_IS_ON(2)) {
+    cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
+  }
+  if (cc_.status != CHOLMOD_OK) {
+    *message =
+        StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
+    return nullptr;
+  }
+
+  CHECK(factor != nullptr);
+  return factor;
+}
+
+bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
+                                   const vector<int>& row_blocks,
+                                   const vector<int>& col_blocks,
+                                   vector<int>* ordering) {
+  const int num_row_blocks = row_blocks.size();
+  const int num_col_blocks = col_blocks.size();
+
+  // Arrays storing the compressed column structure of the matrix
+  // incoding the block sparsity of A.
+  vector<int> block_cols;
+  vector<int> block_rows;
+
+  CompressedColumnScalarMatrixToBlockMatrix(reinterpret_cast<const int*>(A->i),
+                                            reinterpret_cast<const int*>(A->p),
+                                            row_blocks,
+                                            col_blocks,
+                                            &block_rows,
+                                            &block_cols);
+  cholmod_sparse_struct block_matrix;
+  block_matrix.nrow = num_row_blocks;
+  block_matrix.ncol = num_col_blocks;
+  block_matrix.nzmax = block_rows.size();
+  block_matrix.p = reinterpret_cast<void*>(&block_cols[0]);
+  block_matrix.i = reinterpret_cast<void*>(&block_rows[0]);
+  block_matrix.x = nullptr;
+  block_matrix.stype = A->stype;
+  block_matrix.itype = CHOLMOD_INT;
+  block_matrix.xtype = CHOLMOD_PATTERN;
+  block_matrix.dtype = CHOLMOD_DOUBLE;
+  block_matrix.sorted = 1;
+  block_matrix.packed = 1;
+
+  vector<int> block_ordering(num_row_blocks);
+  if (!cholmod_amd(&block_matrix, nullptr, 0, &block_ordering[0], &cc_)) {
+    return false;
+  }
+
+  BlockOrderingToScalarOrdering(row_blocks, block_ordering, ordering);
+  return true;
+}
+
+LinearSolverTerminationType SuiteSparse::Cholesky(cholmod_sparse* A,
+                                                  cholmod_factor* L,
+                                                  string* message) {
+  CHECK(A != nullptr);
+  CHECK(L != nullptr);
+
+  // Save the current print level and silence CHOLMOD, otherwise
+  // CHOLMOD is prone to dumping stuff to stderr, which can be
+  // distracting when the error (matrix is indefinite) is not a fatal
+  // failure.
+  const int old_print_level = cc_.print;
+  cc_.print = 0;
+
+  cc_.quick_return_if_not_posdef = 1;
+  int cholmod_status = cholmod_factorize(A, L, &cc_);
+  cc_.print = old_print_level;
+
+  switch (cc_.status) {
+    case CHOLMOD_NOT_INSTALLED:
+      *message = "CHOLMOD failure: Method not installed.";
+      return LINEAR_SOLVER_FATAL_ERROR;
+    case CHOLMOD_OUT_OF_MEMORY:
+      *message = "CHOLMOD failure: Out of memory.";
+      return LINEAR_SOLVER_FATAL_ERROR;
+    case CHOLMOD_TOO_LARGE:
+      *message = "CHOLMOD failure: Integer overflow occurred.";
+      return LINEAR_SOLVER_FATAL_ERROR;
+    case CHOLMOD_INVALID:
+      *message = "CHOLMOD failure: Invalid input.";
+      return LINEAR_SOLVER_FATAL_ERROR;
+    case CHOLMOD_NOT_POSDEF:
+      *message = "CHOLMOD warning: Matrix not positive definite.";
+      return LINEAR_SOLVER_FAILURE;
+    case CHOLMOD_DSMALL:
+      *message =
+          "CHOLMOD warning: D for LDL' or diag(L) or "
+          "LL' has tiny absolute value.";
+      return LINEAR_SOLVER_FAILURE;
+    case CHOLMOD_OK:
+      if (cholmod_status != 0) {
+        return LINEAR_SOLVER_SUCCESS;
+      }
+
+      *message =
+          "CHOLMOD failure: cholmod_factorize returned false "
+          "but cholmod_common::status is CHOLMOD_OK."
+          "Please report this to ceres-solver@googlegroups.com.";
+      return LINEAR_SOLVER_FATAL_ERROR;
+    default:
+      *message = StringPrintf(
+          "Unknown cholmod return code: %d. "
+          "Please report this to ceres-solver@googlegroups.com.",
+          cc_.status);
+      return LINEAR_SOLVER_FATAL_ERROR;
+  }
+
+  return LINEAR_SOLVER_FATAL_ERROR;
+}
+
+cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
+                                  cholmod_dense* b,
+                                  string* message) {
+  if (cc_.status != CHOLMOD_OK) {
+    *message = "cholmod_solve failed. CHOLMOD status is not CHOLMOD_OK";
+    return nullptr;
+  }
+
+  return cholmod_solve(CHOLMOD_A, L, b, &cc_);
+}
+
+bool SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
+                                                   int* ordering) {
+  return cholmod_amd(matrix, nullptr, 0, ordering, &cc_);
+}
+
+bool SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
+    cholmod_sparse* matrix, int* constraints, int* ordering) {
+#ifndef CERES_NO_CAMD
+  return cholmod_camd(matrix, nullptr, 0, constraints, ordering, &cc_);
+#else
+  LOG(FATAL) << "Congratulations you have found a bug in Ceres."
+             << "Ceres Solver was compiled with SuiteSparse "
+             << "version 4.1.0 or less. Calling this function "
+             << "in that case is a bug. Please contact the"
+             << "the Ceres Solver developers.";
+  return false;
+#endif
+}
+
+std::unique_ptr<SparseCholesky> SuiteSparseCholesky::Create(
+    const OrderingType ordering_type) {
+  return std::unique_ptr<SparseCholesky>(new SuiteSparseCholesky(ordering_type));
+}
+
+SuiteSparseCholesky::SuiteSparseCholesky(const OrderingType ordering_type)
+    : ordering_type_(ordering_type), factor_(nullptr) {}
+
+SuiteSparseCholesky::~SuiteSparseCholesky() {
+  if (factor_ != nullptr) {
+    ss_.Free(factor_);
+  }
+}
+
+LinearSolverTerminationType SuiteSparseCholesky::Factorize(
+    CompressedRowSparseMatrix* lhs, string* message) {
+  if (lhs == nullptr) {
+    *message = "Failure: Input lhs is NULL.";
+    return LINEAR_SOLVER_FATAL_ERROR;
+  }
+
+  cholmod_sparse cholmod_lhs = ss_.CreateSparseMatrixTransposeView(lhs);
+
+  if (factor_ == nullptr) {
+    if (ordering_type_ == NATURAL) {
+      factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&cholmod_lhs, message);
+    } else {
+      if (!lhs->col_blocks().empty() && !(lhs->row_blocks().empty())) {
+        factor_ = ss_.BlockAnalyzeCholesky(
+            &cholmod_lhs, lhs->col_blocks(), lhs->row_blocks(), message);
+      } else {
+        factor_ = ss_.AnalyzeCholesky(&cholmod_lhs, message);
+      }
+    }
+
+    if (factor_ == nullptr) {
+      return LINEAR_SOLVER_FATAL_ERROR;
+    }
+  }
+
+  return ss_.Cholesky(&cholmod_lhs, factor_, message);
+}
+
+CompressedRowSparseMatrix::StorageType SuiteSparseCholesky::StorageType()
+    const {
+  return ((ordering_type_ == NATURAL)
+              ? CompressedRowSparseMatrix::UPPER_TRIANGULAR
+              : CompressedRowSparseMatrix::LOWER_TRIANGULAR);
+}
+
+LinearSolverTerminationType SuiteSparseCholesky::Solve(const double* rhs,
+                                                       double* solution,
+                                                       string* message) {
+  // Error checking
+  if (factor_ == nullptr) {
+    *message = "Solve called without a call to Factorize first.";
+    return LINEAR_SOLVER_FATAL_ERROR;
+  }
+
+  const int num_cols = factor_->n;
+  cholmod_dense cholmod_rhs = ss_.CreateDenseVectorView(rhs, num_cols);
+  cholmod_dense* cholmod_dense_solution =
+      ss_.Solve(factor_, &cholmod_rhs, message);
+
+  if (cholmod_dense_solution == nullptr) {
+    return LINEAR_SOLVER_FAILURE;
+  }
+
+  memcpy(solution, cholmod_dense_solution->x, num_cols * sizeof(*solution));
+  ss_.Free(cholmod_dense_solution);
+  return LINEAR_SOLVER_SUCCESS;
+}
+
+}  // namespace internal
+}  // namespace ceres
+
+#endif  // CERES_NO_SUITESPARSE