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diff --git a/internal/ceres/unsymmetric_linear_solver_test.cc b/internal/ceres/unsymmetric_linear_solver_test.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/casts.h"
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/linear_least_squares_problems.h"
+#include "ceres/linear_solver.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/types.h"
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+
+namespace ceres {
+namespace internal {
+
+class UnsymmetricLinearSolverTest : public ::testing::Test {
+ protected :
+  virtual void SetUp() {
+    scoped_ptr<LinearLeastSquaresProblem> problem(
+        CreateLinearLeastSquaresProblemFromId(0));
+
+    CHECK_NOTNULL(problem.get());
+    A_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
+    b_.reset(problem->b.release());
+    D_.reset(problem->D.release());
+    sol_unregularized_.reset(problem->x.release());
+    sol_regularized_.reset(problem->x_D.release());
+  }
+
+  void TestSolver(
+      LinearSolverType linear_solver_type,
+      SparseLinearAlgebraLibraryType sparse_linear_algebra_library) {
+    LinearSolver::Options options;
+    options.type = linear_solver_type;
+    options.sparse_linear_algebra_library = sparse_linear_algebra_library;
+    options.use_block_amd = false;
+    scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
+
+    LinearSolver::PerSolveOptions per_solve_options;
+    LinearSolver::Summary unregularized_solve_summary;
+    LinearSolver::Summary regularized_solve_summary;
+    Vector x_unregularized(A_->num_cols());
+    Vector x_regularized(A_->num_cols());
+
+    scoped_ptr<SparseMatrix> transformed_A;
+
+    if (linear_solver_type == DENSE_QR ||
+        linear_solver_type == DENSE_NORMAL_CHOLESKY) {
+      transformed_A.reset(new DenseSparseMatrix(*A_));
+    } else if (linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
+      transformed_A.reset(new   CompressedRowSparseMatrix(*A_));
+    } else {
+      LOG(FATAL) << "Unknown linear solver : " << linear_solver_type;
+    }
+    // Unregularized
+    unregularized_solve_summary =
+        solver->Solve(transformed_A.get(),
+                      b_.get(),
+                      per_solve_options,
+                      x_unregularized.data());
+
+    // Regularized solution
+    per_solve_options.D = D_.get();
+    regularized_solve_summary =
+        solver->Solve(transformed_A.get(),
+                      b_.get(),
+                      per_solve_options,
+                      x_regularized.data());
+
+    EXPECT_EQ(unregularized_solve_summary.termination_type, TOLERANCE);
+
+    for (int i = 0; i < A_->num_cols(); ++i) {
+      EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8);
+    }
+
+    EXPECT_EQ(regularized_solve_summary.termination_type, TOLERANCE);
+    for (int i = 0; i < A_->num_cols(); ++i) {
+      EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8);
+    }
+  }
+
+  scoped_ptr<TripletSparseMatrix> A_;
+  scoped_array<double> b_;
+  scoped_array<double> D_;
+  scoped_array<double> sol_unregularized_;
+  scoped_array<double> sol_regularized_;
+};
+
+TEST_F(UnsymmetricLinearSolverTest, DenseQR) {
+  TestSolver(DENSE_QR, SUITE_SPARSE);
+}
+
+TEST_F(UnsymmetricLinearSolverTest, DenseNormalCholesky) {
+  TestSolver(DENSE_NORMAL_CHOLESKY, SUITE_SPARSE);
+}
+
+#ifndef CERES_NO_SUITESPARSE
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingSuiteSparse) {
+  TestSolver(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE);
+}
+#endif
+
+#ifndef CERES_NO_CXSPARSE
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingCXSparse) {
+  TestSolver(SPARSE_NORMAL_CHOLESKY, CX_SPARSE);
+}
+#endif
+
+}  // namespace internal
+}  // namespace ceres