diff --git a/internal/ceres/covariance_test.cc b/internal/ceres/covariance_test.cc
new file mode 100644
index 0000000..dea0723
--- /dev/null
+++ b/internal/ceres/covariance_test.cc
@@ -0,0 +1,1279 @@
+// 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)
+
+#include "ceres/covariance.h"
+
+#include <algorithm>
+#include <cstdint>
+#include <cmath>
+#include <map>
+#include <memory>
+#include <utility>
+
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/cost_function.h"
+#include "ceres/covariance_impl.h"
+#include "ceres/local_parameterization.h"
+#include "ceres/map_util.h"
+#include "ceres/problem_impl.h"
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+using std::make_pair;
+using std::map;
+using std::pair;
+using std::vector;
+
+class UnaryCostFunction: public CostFunction {
+ public:
+  UnaryCostFunction(const int num_residuals,
+                    const int32_t parameter_block_size,
+                    const double* jacobian)
+      : jacobian_(jacobian, jacobian + num_residuals * parameter_block_size) {
+    set_num_residuals(num_residuals);
+    mutable_parameter_block_sizes()->push_back(parameter_block_size);
+  }
+
+  virtual bool Evaluate(double const* const* parameters,
+                        double* residuals,
+                        double** jacobians) const {
+    for (int i = 0; i < num_residuals(); ++i) {
+      residuals[i] = 1;
+    }
+
+    if (jacobians == NULL) {
+      return true;
+    }
+
+    if (jacobians[0] != NULL) {
+      copy(jacobian_.begin(), jacobian_.end(), jacobians[0]);
+    }
+
+    return true;
+  }
+
+ private:
+  vector<double> jacobian_;
+};
+
+
+class BinaryCostFunction: public CostFunction {
+ public:
+  BinaryCostFunction(const int num_residuals,
+                     const int32_t parameter_block1_size,
+                     const int32_t parameter_block2_size,
+                     const double* jacobian1,
+                     const double* jacobian2)
+      : jacobian1_(jacobian1,
+                   jacobian1 + num_residuals * parameter_block1_size),
+        jacobian2_(jacobian2,
+                   jacobian2 + num_residuals * parameter_block2_size) {
+    set_num_residuals(num_residuals);
+    mutable_parameter_block_sizes()->push_back(parameter_block1_size);
+    mutable_parameter_block_sizes()->push_back(parameter_block2_size);
+  }
+
+  virtual bool Evaluate(double const* const* parameters,
+                        double* residuals,
+                        double** jacobians) const {
+    for (int i = 0; i < num_residuals(); ++i) {
+      residuals[i] = 2;
+    }
+
+    if (jacobians == NULL) {
+      return true;
+    }
+
+    if (jacobians[0] != NULL) {
+      copy(jacobian1_.begin(), jacobian1_.end(), jacobians[0]);
+    }
+
+    if (jacobians[1] != NULL) {
+      copy(jacobian2_.begin(), jacobian2_.end(), jacobians[1]);
+    }
+
+    return true;
+  }
+
+ private:
+  vector<double> jacobian1_;
+  vector<double> jacobian2_;
+};
+
+// x_plus_delta = delta * x;
+class PolynomialParameterization : public LocalParameterization {
+ public:
+  virtual ~PolynomialParameterization() {}
+
+  virtual bool Plus(const double* x,
+                    const double* delta,
+                    double* x_plus_delta) const {
+    x_plus_delta[0] = delta[0] * x[0];
+    x_plus_delta[1] = delta[0] * x[1];
+    return true;
+  }
+
+  virtual bool ComputeJacobian(const double* x, double* jacobian) const {
+    jacobian[0] = x[0];
+    jacobian[1] = x[1];
+    return true;
+  }
+
+  virtual int GlobalSize() const { return 2; }
+  virtual int LocalSize() const { return 1; }
+};
+
+TEST(CovarianceImpl, ComputeCovarianceSparsity) {
+  double parameters[10];
+
+  double* block1 = parameters;
+  double* block2 = block1 + 1;
+  double* block3 = block2 + 2;
+  double* block4 = block3 + 3;
+
+  ProblemImpl problem;
+
+  // Add in random order
+  Vector junk_jacobian = Vector::Zero(10);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 1, junk_jacobian.data()), NULL, block1);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 4, junk_jacobian.data()), NULL, block4);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 3, junk_jacobian.data()), NULL, block3);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 2, junk_jacobian.data()), NULL, block2);
+
+  // Sparsity pattern
+  //
+  // Note that the problem structure does not imply this sparsity
+  // pattern since all the residual blocks are unary. But the
+  // ComputeCovarianceSparsity function in its current incarnation
+  // does not pay attention to this fact and only looks at the
+  // parameter block pairs that the user provides.
+  //
+  //  X . . . . . X X X X
+  //  . X X X X X . . . .
+  //  . X X X X X . . . .
+  //  . . . X X X . . . .
+  //  . . . X X X . . . .
+  //  . . . X X X . . . .
+  //  . . . . . . X X X X
+  //  . . . . . . X X X X
+  //  . . . . . . X X X X
+  //  . . . . . . X X X X
+
+  int expected_rows[] = {0, 5, 10, 15, 18, 21, 24, 28, 32, 36, 40};
+  int expected_cols[] = {0, 6, 7, 8, 9,
+                         1, 2, 3, 4, 5,
+                         1, 2, 3, 4, 5,
+                         3, 4, 5,
+                         3, 4, 5,
+                         3, 4, 5,
+                         6, 7, 8, 9,
+                         6, 7, 8, 9,
+                         6, 7, 8, 9,
+                         6, 7, 8, 9};
+
+
+  vector<pair<const double*, const double*>> covariance_blocks;
+  covariance_blocks.push_back(make_pair(block1, block1));
+  covariance_blocks.push_back(make_pair(block4, block4));
+  covariance_blocks.push_back(make_pair(block2, block2));
+  covariance_blocks.push_back(make_pair(block3, block3));
+  covariance_blocks.push_back(make_pair(block2, block3));
+  covariance_blocks.push_back(make_pair(block4, block1));  // reversed
+
+  Covariance::Options options;
+  CovarianceImpl covariance_impl(options);
+  EXPECT_TRUE(covariance_impl
+              .ComputeCovarianceSparsity(covariance_blocks, &problem));
+
+  const CompressedRowSparseMatrix* crsm = covariance_impl.covariance_matrix();
+
+  EXPECT_EQ(crsm->num_rows(), 10);
+  EXPECT_EQ(crsm->num_cols(), 10);
+  EXPECT_EQ(crsm->num_nonzeros(), 40);
+
+  const int* rows = crsm->rows();
+  for (int r = 0; r < crsm->num_rows() + 1; ++r) {
+    EXPECT_EQ(rows[r], expected_rows[r])
+        << r << " "
+        << rows[r] << " "
+        << expected_rows[r];
+  }
+
+  const int* cols = crsm->cols();
+  for (int c = 0; c < crsm->num_nonzeros(); ++c) {
+    EXPECT_EQ(cols[c], expected_cols[c])
+        << c << " "
+        << cols[c] << " "
+        << expected_cols[c];
+  }
+}
+
+TEST(CovarianceImpl, ComputeCovarianceSparsityWithConstantParameterBlock) {
+  double parameters[10];
+
+  double* block1 = parameters;
+  double* block2 = block1 + 1;
+  double* block3 = block2 + 2;
+  double* block4 = block3 + 3;
+
+  ProblemImpl problem;
+
+  // Add in random order
+  Vector junk_jacobian = Vector::Zero(10);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 1, junk_jacobian.data()), NULL, block1);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 4, junk_jacobian.data()), NULL, block4);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 3, junk_jacobian.data()), NULL, block3);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 2, junk_jacobian.data()), NULL, block2);
+  problem.SetParameterBlockConstant(block3);
+
+  // Sparsity pattern
+  //
+  // Note that the problem structure does not imply this sparsity
+  // pattern since all the residual blocks are unary. But the
+  // ComputeCovarianceSparsity function in its current incarnation
+  // does not pay attention to this fact and only looks at the
+  // parameter block pairs that the user provides.
+  //
+  //  X . . X X X X
+  //  . X X . . . .
+  //  . X X . . . .
+  //  . . . X X X X
+  //  . . . X X X X
+  //  . . . X X X X
+  //  . . . X X X X
+
+  int expected_rows[] = {0, 5, 7, 9, 13, 17, 21, 25};
+  int expected_cols[] = {0, 3, 4, 5, 6,
+                         1, 2,
+                         1, 2,
+                         3, 4, 5, 6,
+                         3, 4, 5, 6,
+                         3, 4, 5, 6,
+                         3, 4, 5, 6};
+
+  vector<pair<const double*, const double*>> covariance_blocks;
+  covariance_blocks.push_back(make_pair(block1, block1));
+  covariance_blocks.push_back(make_pair(block4, block4));
+  covariance_blocks.push_back(make_pair(block2, block2));
+  covariance_blocks.push_back(make_pair(block3, block3));
+  covariance_blocks.push_back(make_pair(block2, block3));
+  covariance_blocks.push_back(make_pair(block4, block1));  // reversed
+
+  Covariance::Options options;
+  CovarianceImpl covariance_impl(options);
+  EXPECT_TRUE(covariance_impl
+              .ComputeCovarianceSparsity(covariance_blocks, &problem));
+
+  const CompressedRowSparseMatrix* crsm = covariance_impl.covariance_matrix();
+
+  EXPECT_EQ(crsm->num_rows(), 7);
+  EXPECT_EQ(crsm->num_cols(), 7);
+  EXPECT_EQ(crsm->num_nonzeros(), 25);
+
+  const int* rows = crsm->rows();
+  for (int r = 0; r < crsm->num_rows() + 1; ++r) {
+    EXPECT_EQ(rows[r], expected_rows[r])
+        << r << " "
+        << rows[r] << " "
+        << expected_rows[r];
+  }
+
+  const int* cols = crsm->cols();
+  for (int c = 0; c < crsm->num_nonzeros(); ++c) {
+    EXPECT_EQ(cols[c], expected_cols[c])
+        << c << " "
+        << cols[c] << " "
+        << expected_cols[c];
+  }
+}
+
+TEST(CovarianceImpl, ComputeCovarianceSparsityWithFreeParameterBlock) {
+  double parameters[10];
+
+  double* block1 = parameters;
+  double* block2 = block1 + 1;
+  double* block3 = block2 + 2;
+  double* block4 = block3 + 3;
+
+  ProblemImpl problem;
+
+  // Add in random order
+  Vector junk_jacobian = Vector::Zero(10);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 1, junk_jacobian.data()), NULL, block1);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 4, junk_jacobian.data()), NULL, block4);
+  problem.AddParameterBlock(block3, 3);
+  problem.AddResidualBlock(
+      new UnaryCostFunction(1, 2, junk_jacobian.data()), NULL, block2);
+
+  // Sparsity pattern
+  //
+  // Note that the problem structure does not imply this sparsity
+  // pattern since all the residual blocks are unary. But the
+  // ComputeCovarianceSparsity function in its current incarnation
+  // does not pay attention to this fact and only looks at the
+  // parameter block pairs that the user provides.
+  //
+  //  X . . X X X X
+  //  . X X . . . .
+  //  . X X . . . .
+  //  . . . X X X X
+  //  . . . X X X X
+  //  . . . X X X X
+  //  . . . X X X X
+
+  int expected_rows[] = {0, 5, 7, 9, 13, 17, 21, 25};
+  int expected_cols[] = {0, 3, 4, 5, 6,
+                         1, 2,
+                         1, 2,
+                         3, 4, 5, 6,
+                         3, 4, 5, 6,
+                         3, 4, 5, 6,
+                         3, 4, 5, 6};
+
+  vector<pair<const double*, const double*>> covariance_blocks;
+  covariance_blocks.push_back(make_pair(block1, block1));
+  covariance_blocks.push_back(make_pair(block4, block4));
+  covariance_blocks.push_back(make_pair(block2, block2));
+  covariance_blocks.push_back(make_pair(block3, block3));
+  covariance_blocks.push_back(make_pair(block2, block3));
+  covariance_blocks.push_back(make_pair(block4, block1));  // reversed
+
+  Covariance::Options options;
+  CovarianceImpl covariance_impl(options);
+  EXPECT_TRUE(covariance_impl
+              .ComputeCovarianceSparsity(covariance_blocks, &problem));
+
+  const CompressedRowSparseMatrix* crsm = covariance_impl.covariance_matrix();
+
+  EXPECT_EQ(crsm->num_rows(), 7);
+  EXPECT_EQ(crsm->num_cols(), 7);
+  EXPECT_EQ(crsm->num_nonzeros(), 25);
+
+  const int* rows = crsm->rows();
+  for (int r = 0; r < crsm->num_rows() + 1; ++r) {
+    EXPECT_EQ(rows[r], expected_rows[r])
+        << r << " "
+        << rows[r] << " "
+        << expected_rows[r];
+  }
+
+  const int* cols = crsm->cols();
+  for (int c = 0; c < crsm->num_nonzeros(); ++c) {
+    EXPECT_EQ(cols[c], expected_cols[c])
+        << c << " "
+        << cols[c] << " "
+        << expected_cols[c];
+  }
+}
+
+class CovarianceTest : public ::testing::Test {
+ protected:
+  typedef map<const double*, pair<int, int>> BoundsMap;
+
+  virtual void SetUp() {
+    double* x = parameters_;
+    double* y = x + 2;
+    double* z = y + 3;
+
+    x[0] = 1;
+    x[1] = 1;
+    y[0] = 2;
+    y[1] = 2;
+    y[2] = 2;
+    z[0] = 3;
+
+    {
+      double jacobian[] = { 1.0, 0.0, 0.0, 1.0};
+      problem_.AddResidualBlock(new UnaryCostFunction(2, 2, jacobian), NULL, x);
+    }
+
+    {
+      double jacobian[] = { 2.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 2.0 };
+      problem_.AddResidualBlock(new UnaryCostFunction(3, 3, jacobian), NULL, y);
+    }
+
+    {
+      double jacobian = 5.0;
+      problem_.AddResidualBlock(new UnaryCostFunction(1, 1, &jacobian),
+                                NULL,
+                                z);
+    }
+
+    {
+      double jacobian1[] = { 1.0, 2.0, 3.0 };
+      double jacobian2[] = { -5.0, -6.0 };
+      problem_.AddResidualBlock(
+          new BinaryCostFunction(1, 3, 2, jacobian1, jacobian2),
+          NULL,
+          y,
+          x);
+    }
+
+    {
+      double jacobian1[] = {2.0 };
+      double jacobian2[] = { 3.0, -2.0 };
+      problem_.AddResidualBlock(
+          new BinaryCostFunction(1, 1, 2, jacobian1, jacobian2),
+          NULL,
+          z,
+          x);
+    }
+
+    all_covariance_blocks_.push_back(make_pair(x, x));
+    all_covariance_blocks_.push_back(make_pair(y, y));
+    all_covariance_blocks_.push_back(make_pair(z, z));
+    all_covariance_blocks_.push_back(make_pair(x, y));
+    all_covariance_blocks_.push_back(make_pair(x, z));
+    all_covariance_blocks_.push_back(make_pair(y, z));
+
+    column_bounds_[x] = make_pair(0, 2);
+    column_bounds_[y] = make_pair(2, 5);
+    column_bounds_[z] = make_pair(5, 6);
+  }
+
+  // Computes covariance in ambient space.
+  void ComputeAndCompareCovarianceBlocks(const Covariance::Options& options,
+                                         const double* expected_covariance) {
+    ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace(
+        options,
+        true,  // ambient
+        expected_covariance);
+  }
+
+  // Computes covariance in tangent space.
+  void ComputeAndCompareCovarianceBlocksInTangentSpace(
+                                         const Covariance::Options& options,
+                                         const double* expected_covariance) {
+    ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace(
+        options,
+        false,  // tangent
+        expected_covariance);
+  }
+
+  void ComputeAndCompareCovarianceBlocksInTangentOrAmbientSpace(
+      const Covariance::Options& options,
+      bool lift_covariance_to_ambient_space,
+      const double* expected_covariance) {
+    // Generate all possible combination of block pairs and check if the
+    // covariance computation is correct.
+    for (int i = 0; i <= 64; ++i) {
+      vector<pair<const double*, const double*>> covariance_blocks;
+      if (i & 1) {
+        covariance_blocks.push_back(all_covariance_blocks_[0]);
+      }
+
+      if (i & 2) {
+        covariance_blocks.push_back(all_covariance_blocks_[1]);
+      }
+
+      if (i & 4) {
+        covariance_blocks.push_back(all_covariance_blocks_[2]);
+      }
+
+      if (i & 8) {
+        covariance_blocks.push_back(all_covariance_blocks_[3]);
+      }
+
+      if (i & 16) {
+        covariance_blocks.push_back(all_covariance_blocks_[4]);
+      }
+
+      if (i & 32) {
+        covariance_blocks.push_back(all_covariance_blocks_[5]);
+      }
+
+      Covariance covariance(options);
+      EXPECT_TRUE(covariance.Compute(covariance_blocks, &problem_));
+
+      for (int i = 0; i < covariance_blocks.size(); ++i) {
+        const double* block1 = covariance_blocks[i].first;
+        const double* block2 = covariance_blocks[i].second;
+        // block1, block2
+        GetCovarianceBlockAndCompare(block1,
+                                     block2,
+                                     lift_covariance_to_ambient_space,
+                                     covariance,
+                                     expected_covariance);
+        // block2, block1
+        GetCovarianceBlockAndCompare(block2,
+                                     block1,
+                                     lift_covariance_to_ambient_space,
+                                     covariance,
+                                     expected_covariance);
+      }
+    }
+  }
+
+  void GetCovarianceBlockAndCompare(const double* block1,
+                                    const double* block2,
+                                    bool lift_covariance_to_ambient_space,
+                                    const Covariance& covariance,
+                                    const double* expected_covariance) {
+    const BoundsMap& column_bounds = lift_covariance_to_ambient_space ?
+        column_bounds_ : local_column_bounds_;
+    const int row_begin = FindOrDie(column_bounds, block1).first;
+    const int row_end = FindOrDie(column_bounds, block1).second;
+    const int col_begin = FindOrDie(column_bounds, block2).first;
+    const int col_end = FindOrDie(column_bounds, block2).second;
+
+    Matrix actual(row_end - row_begin, col_end - col_begin);
+    if (lift_covariance_to_ambient_space) {
+      EXPECT_TRUE(covariance.GetCovarianceBlock(block1,
+                                                block2,
+                                                actual.data()));
+    } else {
+      EXPECT_TRUE(covariance.GetCovarianceBlockInTangentSpace(block1,
+                                                              block2,
+                                                              actual.data()));
+    }
+
+    int dof = 0;  // degrees of freedom = sum of LocalSize()s
+    for (const auto& bound : column_bounds) {
+      dof = std::max(dof, bound.second.second);
+    }
+    ConstMatrixRef expected(expected_covariance, dof, dof);
+    double diff_norm = (expected.block(row_begin,
+                                       col_begin,
+                                       row_end - row_begin,
+                                       col_end - col_begin) - actual).norm();
+    diff_norm /= (row_end - row_begin) * (col_end - col_begin);
+
+    const double kTolerance = 1e-5;
+    EXPECT_NEAR(diff_norm, 0.0, kTolerance)
+        << "rows: " << row_begin << " " << row_end << "  "
+        << "cols: " << col_begin << " " << col_end << "  "
+        << "\n\n expected: \n " << expected.block(row_begin,
+                                                  col_begin,
+                                                  row_end - row_begin,
+                                                  col_end - col_begin)
+        << "\n\n actual: \n " << actual
+        << "\n\n full expected: \n" << expected;
+  }
+
+  double parameters_[6];
+  Problem problem_;
+  vector<pair<const double*, const double*>> all_covariance_blocks_;
+  BoundsMap column_bounds_;
+  BoundsMap local_column_bounds_;
+};
+
+
+TEST_F(CovarianceTest, NormalBehavior) {
+  // J
+  //
+  //   1  0  0  0  0  0
+  //   0  1  0  0  0  0
+  //   0  0  2  0  0  0
+  //   0  0  0  2  0  0
+  //   0  0  0  0  2  0
+  //   0  0  0  0  0  5
+  //  -5 -6  1  2  3  0
+  //   3 -2  0  0  0  2
+
+  // J'J
+  //
+  //   35  24 -5 -10 -15  6
+  //   24  41 -6 -12 -18 -4
+  //   -5  -6  5   2   3  0
+  //  -10 -12  2   8   6  0
+  //  -15 -18  3   6  13  0
+  //    6  -4  0   0   0 29
+
+  // inv(J'J) computed using octave.
+  double expected_covariance[] = {
+     7.0747e-02,  -8.4923e-03,   1.6821e-02,   3.3643e-02,   5.0464e-02,  -1.5809e-02,  // NOLINT
+    -8.4923e-03,   8.1352e-02,   2.4758e-02,   4.9517e-02,   7.4275e-02,   1.2978e-02,  // NOLINT
+     1.6821e-02,   2.4758e-02,   2.4904e-01,  -1.9271e-03,  -2.8906e-03,  -6.5325e-05,  // NOLINT
+     3.3643e-02,   4.9517e-02,  -1.9271e-03,   2.4615e-01,  -5.7813e-03,  -1.3065e-04,  // NOLINT
+     5.0464e-02,   7.4275e-02,  -2.8906e-03,  -5.7813e-03,   2.4133e-01,  -1.9598e-04,  // NOLINT
+    -1.5809e-02,   1.2978e-02,  -6.5325e-05,  -1.3065e-04,  -1.9598e-04,   3.9544e-02,  // NOLINT
+  };
+
+  Covariance::Options options;
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+}
+
+#ifdef CERES_USE_OPENMP
+
+TEST_F(CovarianceTest, ThreadedNormalBehavior) {
+  // J
+  //
+  //   1  0  0  0  0  0
+  //   0  1  0  0  0  0
+  //   0  0  2  0  0  0
+  //   0  0  0  2  0  0
+  //   0  0  0  0  2  0
+  //   0  0  0  0  0  5
+  //  -5 -6  1  2  3  0
+  //   3 -2  0  0  0  2
+
+  // J'J
+  //
+  //   35  24 -5 -10 -15  6
+  //   24  41 -6 -12 -18 -4
+  //   -5  -6  5   2   3  0
+  //  -10 -12  2   8   6  0
+  //  -15 -18  3   6  13  0
+  //    6  -4  0   0   0 29
+
+  // inv(J'J) computed using octave.
+  double expected_covariance[] = {
+     7.0747e-02,  -8.4923e-03,   1.6821e-02,   3.3643e-02,   5.0464e-02,  -1.5809e-02,  // NOLINT
+    -8.4923e-03,   8.1352e-02,   2.4758e-02,   4.9517e-02,   7.4275e-02,   1.2978e-02,  // NOLINT
+     1.6821e-02,   2.4758e-02,   2.4904e-01,  -1.9271e-03,  -2.8906e-03,  -6.5325e-05,  // NOLINT
+     3.3643e-02,   4.9517e-02,  -1.9271e-03,   2.4615e-01,  -5.7813e-03,  -1.3065e-04,  // NOLINT
+     5.0464e-02,   7.4275e-02,  -2.8906e-03,  -5.7813e-03,   2.4133e-01,  -1.9598e-04,  // NOLINT
+    -1.5809e-02,   1.2978e-02,  -6.5325e-05,  -1.3065e-04,  -1.9598e-04,   3.9544e-02,  // NOLINT
+  };
+
+  Covariance::Options options;
+  options.num_threads = 4;
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+}
+
+#endif  // CERES_USE_OPENMP
+
+TEST_F(CovarianceTest, ConstantParameterBlock) {
+  problem_.SetParameterBlockConstant(parameters_);
+
+  // J
+  //
+  //  0  0  0  0  0  0
+  //  0  0  0  0  0  0
+  //  0  0  2  0  0  0
+  //  0  0  0  2  0  0
+  //  0  0  0  0  2  0
+  //  0  0  0  0  0  5
+  //  0  0  1  2  3  0
+  //  0  0  0  0  0  2
+
+  // J'J
+  //
+  //  0  0  0  0  0  0
+  //  0  0  0  0  0  0
+  //  0  0  5  2  3  0
+  //  0  0  2  8  6  0
+  //  0  0  3  6 13  0
+  //  0  0  0  0  0 29
+
+  // pinv(J'J) computed using octave.
+  double expected_covariance[] = {
+              0,            0,            0,            0,            0,            0,  // NOLINT
+              0,            0,            0,            0,            0,            0,  // NOLINT
+              0,            0,      0.23611,     -0.02778,     -0.04167,     -0.00000,  // NOLINT
+              0,            0,     -0.02778,      0.19444,     -0.08333,     -0.00000,  // NOLINT
+              0,            0,     -0.04167,     -0.08333,      0.12500,     -0.00000,  // NOLINT
+              0,            0,     -0.00000,     -0.00000,     -0.00000,      0.03448   // NOLINT
+  };
+
+  Covariance::Options options;
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+}
+
+TEST_F(CovarianceTest, LocalParameterization) {
+  double* x = parameters_;
+  double* y = x + 2;
+
+  problem_.SetParameterization(x, new PolynomialParameterization);
+
+  vector<int> subset;
+  subset.push_back(2);
+  problem_.SetParameterization(y, new SubsetParameterization(3, subset));
+
+  // Raw Jacobian: J
+  //
+  //   1   0  0  0  0  0
+  //   0   1  0  0  0  0
+  //   0   0  2  0  0  0
+  //   0   0  0  2  0  0
+  //   0   0  0  0  2  0
+  //   0   0  0  0  0  5
+  //  -5  -6  1  2  3  0
+  //   3  -2  0  0  0  2
+
+  // Local to global jacobian: A
+  //
+  //  1   0   0   0
+  //  1   0   0   0
+  //  0   1   0   0
+  //  0   0   1   0
+  //  0   0   0   0
+  //  0   0   0   1
+
+  // A * inv((J*A)'*(J*A)) * A'
+  // Computed using octave.
+  double expected_covariance[] = {
+    0.01766,   0.01766,   0.02158,   0.04316,   0.00000,  -0.00122,
+    0.01766,   0.01766,   0.02158,   0.04316,   0.00000,  -0.00122,
+    0.02158,   0.02158,   0.24860,  -0.00281,   0.00000,  -0.00149,
+    0.04316,   0.04316,  -0.00281,   0.24439,   0.00000,  -0.00298,
+    0.00000,   0.00000,   0.00000,   0.00000,   0.00000,   0.00000,
+   -0.00122,  -0.00122,  -0.00149,  -0.00298,   0.00000,   0.03457
+  };
+
+  Covariance::Options options;
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+}
+
+TEST_F(CovarianceTest, LocalParameterizationInTangentSpace) {
+  double* x = parameters_;
+  double* y = x + 2;
+  double* z = y + 3;
+
+  problem_.SetParameterization(x, new PolynomialParameterization);
+
+  vector<int> subset;
+  subset.push_back(2);
+  problem_.SetParameterization(y, new SubsetParameterization(3, subset));
+
+  local_column_bounds_[x] = make_pair(0, 1);
+  local_column_bounds_[y] = make_pair(1, 3);
+  local_column_bounds_[z] = make_pair(3, 4);
+
+  // Raw Jacobian: J
+  //
+  //   1   0  0  0  0  0
+  //   0   1  0  0  0  0
+  //   0   0  2  0  0  0
+  //   0   0  0  2  0  0
+  //   0   0  0  0  2  0
+  //   0   0  0  0  0  5
+  //  -5  -6  1  2  3  0
+  //   3  -2  0  0  0  2
+
+  // Local to global jacobian: A
+  //
+  //  1   0   0   0
+  //  1   0   0   0
+  //  0   1   0   0
+  //  0   0   1   0
+  //  0   0   0   0
+  //  0   0   0   1
+
+  // inv((J*A)'*(J*A))
+  // Computed using octave.
+  double expected_covariance[] = {
+    0.01766,   0.02158,   0.04316,   -0.00122,
+    0.02158,   0.24860,  -0.00281,   -0.00149,
+    0.04316,  -0.00281,   0.24439,   -0.00298,
+   -0.00122,  -0.00149,  -0.00298,    0.03457  // NOLINT
+  };
+
+  Covariance::Options options;
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+}
+
+TEST_F(CovarianceTest, LocalParameterizationInTangentSpaceWithConstantBlocks) {
+  double* x = parameters_;
+  double* y = x + 2;
+  double* z = y + 3;
+
+  problem_.SetParameterization(x, new PolynomialParameterization);
+  problem_.SetParameterBlockConstant(x);
+
+  vector<int> subset;
+  subset.push_back(2);
+  problem_.SetParameterization(y, new SubsetParameterization(3, subset));
+  problem_.SetParameterBlockConstant(y);
+
+  local_column_bounds_[x] = make_pair(0, 1);
+  local_column_bounds_[y] = make_pair(1, 3);
+  local_column_bounds_[z] = make_pair(3, 4);
+
+  // Raw Jacobian: J
+  //
+  //   1   0  0  0  0  0
+  //   0   1  0  0  0  0
+  //   0   0  2  0  0  0
+  //   0   0  0  2  0  0
+  //   0   0  0  0  2  0
+  //   0   0  0  0  0  5
+  //  -5  -6  1  2  3  0
+  //   3  -2  0  0  0  2
+
+  // Local to global jacobian: A
+  //
+  //  0   0   0   0
+  //  0   0   0   0
+  //  0   0   0   0
+  //  0   0   0   0
+  //  0   0   0   0
+  //  0   0   0   1
+
+  // pinv((J*A)'*(J*A))
+  // Computed using octave.
+  double expected_covariance[] = {
+    0.0, 0.0, 0.0, 0.0,
+    0.0, 0.0, 0.0, 0.0,
+    0.0, 0.0, 0.0, 0.0,
+    0.0, 0.0, 0.0, 0.034482 // NOLINT
+  };
+
+  Covariance::Options options;
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+}
+
+TEST_F(CovarianceTest, TruncatedRank) {
+  // J
+  //
+  //   1  0  0  0  0  0
+  //   0  1  0  0  0  0
+  //   0  0  2  0  0  0
+  //   0  0  0  2  0  0
+  //   0  0  0  0  2  0
+  //   0  0  0  0  0  5
+  //  -5 -6  1  2  3  0
+  //   3 -2  0  0  0  2
+
+  // J'J
+  //
+  //   35  24 -5 -10 -15  6
+  //   24  41 -6 -12 -18 -4
+  //   -5  -6  5   2   3  0
+  //  -10 -12  2   8   6  0
+  //  -15 -18  3   6  13  0
+  //    6  -4  0   0   0 29
+
+  // 3.4142 is the smallest eigen value of J'J. The following matrix
+  // was obtained by dropping the eigenvector corresponding to this
+  // eigenvalue.
+  double expected_covariance[] = {
+     5.4135e-02,  -3.5121e-02,   1.7257e-04,   3.4514e-04,   5.1771e-04,  -1.6076e-02,  // NOLINT
+    -3.5121e-02,   3.8667e-02,  -1.9288e-03,  -3.8576e-03,  -5.7864e-03,   1.2549e-02,  // NOLINT
+     1.7257e-04,  -1.9288e-03,   2.3235e-01,  -3.5297e-02,  -5.2946e-02,  -3.3329e-04,  // NOLINT
+     3.4514e-04,  -3.8576e-03,  -3.5297e-02,   1.7941e-01,  -1.0589e-01,  -6.6659e-04,  // NOLINT
+     5.1771e-04,  -5.7864e-03,  -5.2946e-02,  -1.0589e-01,   9.1162e-02,  -9.9988e-04,  // NOLINT
+    -1.6076e-02,   1.2549e-02,  -3.3329e-04,  -6.6659e-04,  -9.9988e-04,   3.9539e-02   // NOLINT
+  };
+
+
+  {
+    Covariance::Options options;
+    options.algorithm_type = DENSE_SVD;
+    // Force dropping of the smallest eigenvector.
+    options.null_space_rank = 1;
+    ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+  }
+
+  {
+    Covariance::Options options;
+    options.algorithm_type = DENSE_SVD;
+    // Force dropping of the smallest eigenvector via the ratio but
+    // automatic truncation.
+    options.min_reciprocal_condition_number = 0.044494;
+    options.null_space_rank = -1;
+    ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+  }
+}
+
+TEST_F(CovarianceTest, DenseCovarianceMatrixFromSetOfParameters) {
+  Covariance::Options options;
+  Covariance covariance(options);
+  double* x = parameters_;
+  double* y = x + 2;
+  double* z = y + 3;
+  vector<const double*> parameter_blocks;
+  parameter_blocks.push_back(x);
+  parameter_blocks.push_back(y);
+  parameter_blocks.push_back(z);
+  covariance.Compute(parameter_blocks, &problem_);
+  double expected_covariance[36];
+  covariance.GetCovarianceMatrix(parameter_blocks, expected_covariance);
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+}
+
+TEST_F(CovarianceTest, DenseCovarianceMatrixFromSetOfParametersThreaded) {
+  Covariance::Options options;
+  options.num_threads = 4;
+  Covariance covariance(options);
+  double* x = parameters_;
+  double* y = x + 2;
+  double* z = y + 3;
+  vector<const double*> parameter_blocks;
+  parameter_blocks.push_back(x);
+  parameter_blocks.push_back(y);
+  parameter_blocks.push_back(z);
+  covariance.Compute(parameter_blocks, &problem_);
+  double expected_covariance[36];
+  covariance.GetCovarianceMatrix(parameter_blocks, expected_covariance);
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+}
+
+TEST_F(CovarianceTest, DenseCovarianceMatrixFromSetOfParametersInTangentSpace) {
+  Covariance::Options options;
+  Covariance covariance(options);
+  double* x = parameters_;
+  double* y = x + 2;
+  double* z = y + 3;
+
+  problem_.SetParameterization(x, new PolynomialParameterization);
+
+  vector<int> subset;
+  subset.push_back(2);
+  problem_.SetParameterization(y, new SubsetParameterization(3, subset));
+
+  local_column_bounds_[x] = make_pair(0, 1);
+  local_column_bounds_[y] = make_pair(1, 3);
+  local_column_bounds_[z] = make_pair(3, 4);
+
+  vector<const double*> parameter_blocks;
+  parameter_blocks.push_back(x);
+  parameter_blocks.push_back(y);
+  parameter_blocks.push_back(z);
+  covariance.Compute(parameter_blocks, &problem_);
+  double expected_covariance[16];
+  covariance.GetCovarianceMatrixInTangentSpace(parameter_blocks,
+                                               expected_covariance);
+
+#ifndef CERES_NO_SUITESPARSE
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
+
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+#endif
+
+  options.algorithm_type = DENSE_SVD;
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
+  ComputeAndCompareCovarianceBlocksInTangentSpace(options, expected_covariance);
+}
+
+TEST_F(CovarianceTest, ComputeCovarianceFailure) {
+  Covariance::Options options;
+  Covariance covariance(options);
+  double* x = parameters_;
+  double* y = x + 2;
+  vector<const double*> parameter_blocks;
+  parameter_blocks.push_back(x);
+  parameter_blocks.push_back(x);
+  parameter_blocks.push_back(y);
+  parameter_blocks.push_back(y);
+  EXPECT_DEATH_IF_SUPPORTED(covariance.Compute(parameter_blocks, &problem_),
+                            "Covariance::Compute called with duplicate blocks "
+                            "at indices \\(0, 1\\) and \\(2, 3\\)");
+  vector<pair<const double*, const double*>> covariance_blocks;
+  covariance_blocks.push_back(make_pair(x, x));
+  covariance_blocks.push_back(make_pair(x, x));
+  covariance_blocks.push_back(make_pair(y, y));
+  covariance_blocks.push_back(make_pair(y, y));
+  EXPECT_DEATH_IF_SUPPORTED(covariance.Compute(covariance_blocks, &problem_),
+                            "Covariance::Compute called with duplicate blocks "
+                            "at indices \\(0, 1\\) and \\(2, 3\\)");
+}
+
+class RankDeficientCovarianceTest : public CovarianceTest {
+ protected:
+  virtual void SetUp() {
+    double* x = parameters_;
+    double* y = x + 2;
+    double* z = y + 3;
+
+    {
+      double jacobian[] = { 1.0, 0.0, 0.0, 1.0};
+      problem_.AddResidualBlock(new UnaryCostFunction(2, 2, jacobian), NULL, x);
+    }
+
+    {
+      double jacobian[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
+      problem_.AddResidualBlock(new UnaryCostFunction(3, 3, jacobian), NULL, y);
+    }
+
+    {
+      double jacobian = 5.0;
+      problem_.AddResidualBlock(new UnaryCostFunction(1, 1, &jacobian),
+                                NULL,
+                                z);
+    }
+
+    {
+      double jacobian1[] = { 0.0, 0.0, 0.0 };
+      double jacobian2[] = { -5.0, -6.0 };
+      problem_.AddResidualBlock(
+          new BinaryCostFunction(1, 3, 2, jacobian1, jacobian2),
+          NULL,
+          y,
+          x);
+    }
+
+    {
+      double jacobian1[] = {2.0 };
+      double jacobian2[] = { 3.0, -2.0 };
+      problem_.AddResidualBlock(
+          new BinaryCostFunction(1, 1, 2, jacobian1, jacobian2),
+          NULL,
+          z,
+          x);
+    }
+
+    all_covariance_blocks_.push_back(make_pair(x, x));
+    all_covariance_blocks_.push_back(make_pair(y, y));
+    all_covariance_blocks_.push_back(make_pair(z, z));
+    all_covariance_blocks_.push_back(make_pair(x, y));
+    all_covariance_blocks_.push_back(make_pair(x, z));
+    all_covariance_blocks_.push_back(make_pair(y, z));
+
+    column_bounds_[x] = make_pair(0, 2);
+    column_bounds_[y] = make_pair(2, 5);
+    column_bounds_[z] = make_pair(5, 6);
+  }
+};
+
+TEST_F(RankDeficientCovarianceTest, AutomaticTruncation) {
+  // J
+  //
+  //   1  0  0  0  0  0
+  //   0  1  0  0  0  0
+  //   0  0  0  0  0  0
+  //   0  0  0  0  0  0
+  //   0  0  0  0  0  0
+  //   0  0  0  0  0  5
+  //  -5 -6  0  0  0  0
+  //   3 -2  0  0  0  2
+
+  // J'J
+  //
+  //  35 24  0  0  0  6
+  //  24 41  0  0  0 -4
+  //   0  0  0  0  0  0
+  //   0  0  0  0  0  0
+  //   0  0  0  0  0  0
+  //   6 -4  0  0  0 29
+
+  // pinv(J'J) computed using octave.
+  double expected_covariance[] = {
+     0.053998,  -0.033145,   0.000000,   0.000000,   0.000000,  -0.015744,
+    -0.033145,   0.045067,   0.000000,   0.000000,   0.000000,   0.013074,
+     0.000000,   0.000000,   0.000000,   0.000000,   0.000000,   0.000000,
+     0.000000,   0.000000,   0.000000,   0.000000,   0.000000,   0.000000,
+     0.000000,   0.000000,   0.000000,   0.000000,   0.000000,   0.000000,
+    -0.015744,   0.013074,   0.000000,   0.000000,   0.000000,   0.039543
+  };
+
+  Covariance::Options options;
+  options.algorithm_type = DENSE_SVD;
+  options.null_space_rank = -1;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+}
+
+class LargeScaleCovarianceTest : public ::testing::Test {
+ protected:
+  virtual void SetUp() {
+    num_parameter_blocks_ = 2000;
+    parameter_block_size_ = 5;
+    parameters_.reset(
+        new double[parameter_block_size_ * num_parameter_blocks_]);
+
+    Matrix jacobian(parameter_block_size_, parameter_block_size_);
+    for (int i = 0; i < num_parameter_blocks_; ++i) {
+      jacobian.setIdentity();
+      jacobian *= (i + 1);
+
+      double* block_i = parameters_.get() + i * parameter_block_size_;
+      problem_.AddResidualBlock(new UnaryCostFunction(parameter_block_size_,
+                                                      parameter_block_size_,
+                                                      jacobian.data()),
+                                NULL,
+                                block_i);
+      for (int j = i; j < num_parameter_blocks_; ++j) {
+        double* block_j = parameters_.get() + j * parameter_block_size_;
+        all_covariance_blocks_.push_back(make_pair(block_i, block_j));
+      }
+    }
+  }
+
+  void ComputeAndCompare(
+      CovarianceAlgorithmType algorithm_type,
+      SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,
+      int num_threads) {
+    Covariance::Options options;
+    options.algorithm_type = algorithm_type;
+    options.sparse_linear_algebra_library_type =
+        sparse_linear_algebra_library_type;
+    options.num_threads = num_threads;
+    Covariance covariance(options);
+    EXPECT_TRUE(covariance.Compute(all_covariance_blocks_, &problem_));
+
+    Matrix expected(parameter_block_size_, parameter_block_size_);
+    Matrix actual(parameter_block_size_, parameter_block_size_);
+    const double kTolerance = 1e-16;
+
+    for (int i = 0; i < num_parameter_blocks_; ++i) {
+      expected.setIdentity();
+      expected /= (i + 1.0) * (i + 1.0);
+
+      double* block_i = parameters_.get() + i * parameter_block_size_;
+      covariance.GetCovarianceBlock(block_i, block_i, actual.data());
+      EXPECT_NEAR((expected - actual).norm(), 0.0, kTolerance)
+          << "block: " << i << ", " << i << "\n"
+          << "expected: \n" << expected << "\n"
+          << "actual: \n" << actual;
+
+      expected.setZero();
+      for (int j = i + 1; j < num_parameter_blocks_; ++j) {
+        double* block_j = parameters_.get() + j * parameter_block_size_;
+        covariance.GetCovarianceBlock(block_i, block_j, actual.data());
+        EXPECT_NEAR((expected - actual).norm(), 0.0, kTolerance)
+            << "block: " << i << ", " << j << "\n"
+            << "expected: \n" << expected << "\n"
+            << "actual: \n" << actual;
+      }
+    }
+  }
+
+  std::unique_ptr<double[]> parameters_;
+  int parameter_block_size_;
+  int num_parameter_blocks_;
+
+  Problem problem_;
+  vector<pair<const double*, const double*>> all_covariance_blocks_;
+};
+
+#if !defined(CERES_NO_SUITESPARSE) && defined(CERES_USE_OPENMP)
+
+TEST_F(LargeScaleCovarianceTest, Parallel) {
+  ComputeAndCompare(SPARSE_QR, SUITE_SPARSE, 4);
+}
+
+#endif  // !defined(CERES_NO_SUITESPARSE) && defined(CERES_USE_OPENMP)
+
+}  // namespace internal
+}  // namespace ceres