diff --git a/internal/ceres/cxsparse.cc b/internal/ceres/cxsparse.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: strandmark@google.com (Petter Strandmark)
+
+// This include must come before any #ifndef check on Ceres compile options.
+#include "ceres/internal/port.h"
+
+#ifndef CERES_NO_CXSPARSE
+
+#include "ceres/cxsparse.h"
+
+#include <string>
+#include <vector>
+
+#include "ceres/compressed_col_sparse_matrix_utils.h"
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "glog/logging.h"
+
+namespace ceres {
+namespace internal {
+
+using std::vector;
+
+CXSparse::CXSparse() : scratch_(NULL), scratch_size_(0) {}
+
+CXSparse::~CXSparse() {
+  if (scratch_size_ > 0) {
+    cs_di_free(scratch_);
+  }
+}
+
+csn* CXSparse::Cholesky(cs_di* A, cs_dis* symbolic_factor) {
+  return cs_di_chol(A, symbolic_factor);
+}
+
+void CXSparse::Solve(cs_dis* symbolic_factor, csn* numeric_factor, double* b) {
+  // Make sure we have enough scratch space available.
+  const int num_cols = numeric_factor->L->n;
+  if (scratch_size_ < num_cols) {
+    if (scratch_size_ > 0) {
+      cs_di_free(scratch_);
+    }
+    scratch_ =
+        reinterpret_cast<CS_ENTRY*>(cs_di_malloc(num_cols, sizeof(CS_ENTRY)));
+    scratch_size_ = num_cols;
+  }
+
+  // When the Cholesky factor succeeded, these methods are
+  // guaranteed to succeeded as well. In the comments below, "x"
+  // refers to the scratch space.
+  //
+  // Set x = P * b.
+  CHECK(cs_di_ipvec(symbolic_factor->pinv, b, scratch_, num_cols));
+  // Set x = L \ x.
+  CHECK(cs_di_lsolve(numeric_factor->L, scratch_));
+  // Set x = L' \ x.
+  CHECK(cs_di_ltsolve(numeric_factor->L, scratch_));
+  // Set b = P' * x.
+  CHECK(cs_di_pvec(symbolic_factor->pinv, scratch_, b, num_cols));
+}
+
+bool CXSparse::SolveCholesky(cs_di* lhs, double* rhs_and_solution) {
+  return cs_cholsol(1, lhs, rhs_and_solution);
+}
+
+cs_dis* CXSparse::AnalyzeCholesky(cs_di* A) {
+  // order = 1 for Cholesky factor.
+  return cs_schol(1, A);
+}
+
+cs_dis* CXSparse::AnalyzeCholeskyWithNaturalOrdering(cs_di* A) {
+  // order = 0 for Natural ordering.
+  return cs_schol(0, A);
+}
+
+cs_dis* CXSparse::BlockAnalyzeCholesky(cs_di* A,
+                                       const vector<int>& row_blocks,
+                                       const vector<int>& col_blocks) {
+  const int num_row_blocks = row_blocks.size();
+  const int num_col_blocks = col_blocks.size();
+
+  vector<int> block_rows;
+  vector<int> block_cols;
+  CompressedColumnScalarMatrixToBlockMatrix(
+      A->i, A->p, row_blocks, col_blocks, &block_rows, &block_cols);
+  cs_di block_matrix;
+  block_matrix.m = num_row_blocks;
+  block_matrix.n = num_col_blocks;
+  block_matrix.nz = -1;
+  block_matrix.nzmax = block_rows.size();
+  block_matrix.p = &block_cols[0];
+  block_matrix.i = &block_rows[0];
+  block_matrix.x = NULL;
+
+  int* ordering = cs_amd(1, &block_matrix);
+  vector<int> block_ordering(num_row_blocks, -1);
+  std::copy(ordering, ordering + num_row_blocks, &block_ordering[0]);
+  cs_free(ordering);
+
+  vector<int> scalar_ordering;
+  BlockOrderingToScalarOrdering(row_blocks, block_ordering, &scalar_ordering);
+
+  cs_dis* symbolic_factor =
+      reinterpret_cast<cs_dis*>(cs_calloc(1, sizeof(cs_dis)));
+  symbolic_factor->pinv = cs_pinv(&scalar_ordering[0], A->n);
+  cs* permuted_A = cs_symperm(A, symbolic_factor->pinv, 0);
+
+  symbolic_factor->parent = cs_etree(permuted_A, 0);
+  int* postordering = cs_post(symbolic_factor->parent, A->n);
+  int* column_counts =
+      cs_counts(permuted_A, symbolic_factor->parent, postordering, 0);
+  cs_free(postordering);
+  cs_spfree(permuted_A);
+
+  symbolic_factor->cp = (int*)cs_malloc(A->n + 1, sizeof(int));
+  symbolic_factor->lnz = cs_cumsum(symbolic_factor->cp, column_counts, A->n);
+  symbolic_factor->unz = symbolic_factor->lnz;
+
+  cs_free(column_counts);
+
+  if (symbolic_factor->lnz < 0) {
+    cs_sfree(symbolic_factor);
+    symbolic_factor = NULL;
+  }
+
+  return symbolic_factor;
+}
+
+cs_di CXSparse::CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A) {
+  cs_di At;
+  At.m = A->num_cols();
+  At.n = A->num_rows();
+  At.nz = -1;
+  At.nzmax = A->num_nonzeros();
+  At.p = A->mutable_rows();
+  At.i = A->mutable_cols();
+  At.x = A->mutable_values();
+  return At;
+}
+
+cs_di* CXSparse::CreateSparseMatrix(TripletSparseMatrix* tsm) {
+  cs_di_sparse tsm_wrapper;
+  tsm_wrapper.nzmax = tsm->num_nonzeros();
+  tsm_wrapper.nz = tsm->num_nonzeros();
+  tsm_wrapper.m = tsm->num_rows();
+  tsm_wrapper.n = tsm->num_cols();
+  tsm_wrapper.p = tsm->mutable_cols();
+  tsm_wrapper.i = tsm->mutable_rows();
+  tsm_wrapper.x = tsm->mutable_values();
+
+  return cs_compress(&tsm_wrapper);
+}
+
+void CXSparse::ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering) {
+  int* cs_ordering = cs_amd(1, A);
+  std::copy(cs_ordering, cs_ordering + A->m, ordering);
+  cs_free(cs_ordering);
+}
+
+cs_di* CXSparse::TransposeMatrix(cs_di* A) { return cs_di_transpose(A, 1); }
+
+cs_di* CXSparse::MatrixMatrixMultiply(cs_di* A, cs_di* B) {
+  return cs_di_multiply(A, B);
+}
+
+void CXSparse::Free(cs_di* sparse_matrix) { cs_di_spfree(sparse_matrix); }
+
+void CXSparse::Free(cs_dis* symbolic_factor) { cs_di_sfree(symbolic_factor); }
+
+void CXSparse::Free(csn* numeric_factor) { cs_di_nfree(numeric_factor); }
+
+std::unique_ptr<SparseCholesky> CXSparseCholesky::Create(
+    const OrderingType ordering_type) {
+  return std::unique_ptr<SparseCholesky>(new CXSparseCholesky(ordering_type));
+}
+
+CompressedRowSparseMatrix::StorageType CXSparseCholesky::StorageType() const {
+  return CompressedRowSparseMatrix::LOWER_TRIANGULAR;
+}
+
+CXSparseCholesky::CXSparseCholesky(const OrderingType ordering_type)
+    : ordering_type_(ordering_type),
+      symbolic_factor_(NULL),
+      numeric_factor_(NULL) {}
+
+CXSparseCholesky::~CXSparseCholesky() {
+  FreeSymbolicFactorization();
+  FreeNumericFactorization();
+}
+
+LinearSolverTerminationType CXSparseCholesky::Factorize(
+    CompressedRowSparseMatrix* lhs, std::string* message) {
+  CHECK_EQ(lhs->storage_type(), StorageType());
+  if (lhs == NULL) {
+    *message = "Failure: Input lhs is NULL.";
+    return LINEAR_SOLVER_FATAL_ERROR;
+  }
+
+  cs_di cs_lhs = cs_.CreateSparseMatrixTransposeView(lhs);
+
+  if (symbolic_factor_ == NULL) {
+    if (ordering_type_ == NATURAL) {
+      symbolic_factor_ = cs_.AnalyzeCholeskyWithNaturalOrdering(&cs_lhs);
+    } else {
+      if (!lhs->col_blocks().empty() && !(lhs->row_blocks().empty())) {
+        symbolic_factor_ = cs_.BlockAnalyzeCholesky(
+            &cs_lhs, lhs->col_blocks(), lhs->row_blocks());
+      } else {
+        symbolic_factor_ = cs_.AnalyzeCholesky(&cs_lhs);
+      }
+    }
+
+    if (symbolic_factor_ == NULL) {
+      *message = "CXSparse Failure : Symbolic factorization failed.";
+      return LINEAR_SOLVER_FATAL_ERROR;
+    }
+  }
+
+  FreeNumericFactorization();
+  numeric_factor_ = cs_.Cholesky(&cs_lhs, symbolic_factor_);
+  if (numeric_factor_ == NULL) {
+    *message = "CXSparse Failure : Numeric factorization failed.";
+    return LINEAR_SOLVER_FAILURE;
+  }
+
+  return LINEAR_SOLVER_SUCCESS;
+}
+
+LinearSolverTerminationType CXSparseCholesky::Solve(const double* rhs,
+                                                    double* solution,
+                                                    std::string* message) {
+  CHECK(numeric_factor_ != NULL)
+      << "Solve called without a call to Factorize first.";
+  const int num_cols = numeric_factor_->L->n;
+  memcpy(solution, rhs, num_cols * sizeof(*solution));
+  cs_.Solve(symbolic_factor_, numeric_factor_, solution);
+  return LINEAR_SOLVER_SUCCESS;
+}
+
+void CXSparseCholesky::FreeSymbolicFactorization() {
+  if (symbolic_factor_ != NULL) {
+    cs_.Free(symbolic_factor_);
+    symbolic_factor_ = NULL;
+  }
+}
+
+void CXSparseCholesky::FreeNumericFactorization() {
+  if (numeric_factor_ != NULL) {
+    cs_.Free(numeric_factor_);
+    numeric_factor_ = NULL;
+  }
+}
+
+}  // namespace internal
+}  // namespace ceres
+
+#endif  // CERES_NO_CXSPARSE