diff --git a/internal/ceres/compressed_row_sparse_matrix_test.cc b/internal/ceres/compressed_row_sparse_matrix_test.cc
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+++ b/internal/ceres/compressed_row_sparse_matrix_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/compressed_row_sparse_matrix.h"
+
+#include "gtest/gtest.h"
+#include "ceres/casts.h"
+#include "ceres/linear_least_squares_problems.h"
+#include "ceres/matrix_proto.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.h"
+
+namespace ceres {
+namespace internal {
+
+void CompareMatrices(const SparseMatrix* a, const SparseMatrix* b) {
+  EXPECT_EQ(a->num_rows(), b->num_rows());
+  EXPECT_EQ(a->num_cols(), b->num_cols());
+
+  int num_rows = a->num_rows();
+  int num_cols = a->num_cols();
+
+  for (int i = 0; i < num_cols; ++i) {
+    Vector x = Vector::Zero(num_cols);
+    x(i) = 1.0;
+
+    Vector y_a = Vector::Zero(num_rows);
+    Vector y_b = Vector::Zero(num_rows);
+
+    a->RightMultiply(x.data(), y_a.data());
+    b->RightMultiply(x.data(), y_b.data());
+
+    EXPECT_EQ((y_a - y_b).norm(), 0);
+  }
+}
+
+class CompressedRowSparseMatrixTest : public ::testing::Test {
+ protected :
+  virtual void SetUp() {
+    scoped_ptr<LinearLeastSquaresProblem> problem(
+        CreateLinearLeastSquaresProblemFromId(1));
+
+    CHECK_NOTNULL(problem.get());
+
+    tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
+    crsm.reset(new CompressedRowSparseMatrix(*tsm));
+
+    num_rows = tsm->num_rows();
+    num_cols = tsm->num_cols();
+  }
+
+  int num_rows;
+  int num_cols;
+
+  scoped_ptr<TripletSparseMatrix> tsm;
+  scoped_ptr<CompressedRowSparseMatrix> crsm;
+};
+
+TEST_F(CompressedRowSparseMatrixTest, RightMultiply) {
+  CompareMatrices(tsm.get(), crsm.get());
+}
+
+TEST_F(CompressedRowSparseMatrixTest, LeftMultiply) {
+  for (int i = 0; i < num_rows; ++i) {
+    Vector a = Vector::Zero(num_rows);
+    a(i) = 1.0;
+
+    Vector b1 = Vector::Zero(num_cols);
+    Vector b2 = Vector::Zero(num_cols);
+
+    tsm->LeftMultiply(a.data(), b1.data());
+    crsm->LeftMultiply(a.data(), b2.data());
+
+    EXPECT_EQ((b1 - b2).norm(), 0);
+  }
+}
+
+TEST_F(CompressedRowSparseMatrixTest, ColumnNorm) {
+  Vector b1 = Vector::Zero(num_cols);
+  Vector b2 = Vector::Zero(num_cols);
+
+  tsm->SquaredColumnNorm(b1.data());
+  crsm->SquaredColumnNorm(b2.data());
+
+  EXPECT_EQ((b1 - b2).norm(), 0);
+}
+
+TEST_F(CompressedRowSparseMatrixTest, Scale) {
+  Vector scale(num_cols);
+  for (int i = 0; i < num_cols; ++i) {
+    scale(i) = i + 1;
+  }
+
+  tsm->ScaleColumns(scale.data());
+  crsm->ScaleColumns(scale.data());
+  CompareMatrices(tsm.get(), crsm.get());
+}
+
+TEST_F(CompressedRowSparseMatrixTest, DeleteRows) {
+  for (int i = 0; i < num_rows; ++i) {
+    tsm->Resize(num_rows - i, num_cols);
+    crsm->DeleteRows(crsm->num_rows() - tsm->num_rows());
+    CompareMatrices(tsm.get(), crsm.get());
+  }
+}
+
+TEST_F(CompressedRowSparseMatrixTest, AppendRows) {
+  for (int i = 0; i < num_rows; ++i) {
+    TripletSparseMatrix tsm_appendage(*tsm);
+    tsm_appendage.Resize(i, num_cols);
+
+    tsm->AppendRows(tsm_appendage);
+    CompressedRowSparseMatrix crsm_appendage(tsm_appendage);
+    crsm->AppendRows(crsm_appendage);
+
+    CompareMatrices(tsm.get(), crsm.get());
+  }
+}
+
+#ifndef CERES_DONT_HAVE_PROTOCOL_BUFFERS
+TEST_F(CompressedRowSparseMatrixTest, Serialization) {
+  SparseMatrixProto proto;
+  crsm->ToProto(&proto);
+
+  CompressedRowSparseMatrix n(proto);
+  ASSERT_EQ(n.num_rows(), crsm->num_rows());
+  ASSERT_EQ(n.num_cols(), crsm->num_cols());
+  ASSERT_EQ(n.num_nonzeros(), crsm->num_nonzeros());
+
+  for (int i = 0; i < n.num_rows() + 1; ++i) {
+    ASSERT_EQ(crsm->rows()[i], proto.compressed_row_matrix().rows(i));
+    ASSERT_EQ(crsm->rows()[i], n.rows()[i]);
+  }
+
+  for (int i = 0; i < crsm->num_nonzeros(); ++i) {
+    ASSERT_EQ(crsm->cols()[i], proto.compressed_row_matrix().cols(i));
+    ASSERT_EQ(crsm->cols()[i], n.cols()[i]);
+    ASSERT_EQ(crsm->values()[i], proto.compressed_row_matrix().values(i));
+    ASSERT_EQ(crsm->values()[i], n.values()[i]);
+  }
+}
+#endif
+
+TEST_F(CompressedRowSparseMatrixTest, ToDenseMatrix) {
+  Matrix tsm_dense;
+  Matrix crsm_dense;
+
+  tsm->ToDenseMatrix(&tsm_dense);
+  crsm->ToDenseMatrix(&crsm_dense);
+
+  EXPECT_EQ((tsm_dense - crsm_dense).norm(), 0.0);
+}
+
+}  // namespace internal
+}  // namespace ceres