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diff --git a/internal/ceres/block_random_access_diagonal_matrix_test.cc b/internal/ceres/block_random_access_diagonal_matrix_test.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 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 <limits>
+#include <vector>
+
+#include "ceres/block_random_access_diagonal_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+TEST(BlockRandomAccessDiagonalMatrix, GetCell) {
+  vector<int> blocks;
+  blocks.push_back(3);
+  blocks.push_back(4);
+  blocks.push_back(5);
+  const int num_rows = 3 + 4 + 5;
+  const int num_nonzeros =  3 * 3 + 4 * 4 + 5 * 5;
+
+  BlockRandomAccessDiagonalMatrix m(blocks);
+  EXPECT_EQ(m.num_rows(), num_rows);
+  EXPECT_EQ(m.num_cols(), num_rows);
+
+  for (int i = 0; i < blocks.size(); ++i) {
+    const int row_block_id = i;
+    int col_block_id;
+    int row;
+    int col;
+    int row_stride;
+    int col_stride;
+
+    for (int j = 0; j < blocks.size(); ++j) {
+      col_block_id = j;
+      CellInfo* cell =  m.GetCell(row_block_id, col_block_id,
+                                  &row, &col,
+                                  &row_stride, &col_stride);
+      // Off diagonal entries are not present.
+      if (i != j) {
+        EXPECT_TRUE(cell == NULL);
+        continue;
+      }
+
+      EXPECT_TRUE(cell != NULL);
+      EXPECT_EQ(row, 0);
+      EXPECT_EQ(col, 0);
+      EXPECT_EQ(row_stride, blocks[row_block_id]);
+      EXPECT_EQ(col_stride, blocks[col_block_id]);
+
+      // Write into the block
+      MatrixRef(cell->values, row_stride, col_stride).block(
+          row, col, blocks[row_block_id], blocks[col_block_id]) =
+          (row_block_id + 1) * (col_block_id +1) *
+          Matrix::Ones(blocks[row_block_id], blocks[col_block_id]);
+    }
+  }
+
+  const TripletSparseMatrix* tsm = m.matrix();
+  EXPECT_EQ(tsm->num_nonzeros(), num_nonzeros);
+  EXPECT_EQ(tsm->max_num_nonzeros(), num_nonzeros);
+
+  Matrix dense;
+  tsm->ToDenseMatrix(&dense);
+
+  double kTolerance = 1e-14;
+
+  // (0,0)
+  EXPECT_NEAR((dense.block(0, 0, 3, 3) - Matrix::Ones(3, 3)).norm(),
+              0.0,
+              kTolerance);
+
+  // (1,1)
+  EXPECT_NEAR((dense.block(3, 3, 4, 4) - 2 * 2 * Matrix::Ones(4, 4)).norm(),
+              0.0,
+              kTolerance);
+
+  // (1,1)
+  EXPECT_NEAR((dense.block(7, 7, 5, 5) - 3 * 3 * Matrix::Ones(5, 5)).norm(),
+              0.0,
+              kTolerance);
+
+  // There is nothing else in the matrix besides these four blocks.
+  EXPECT_NEAR(dense.norm(), sqrt(9.0 + 16. * 16. + 81.0 * 25.), kTolerance);
+}
+
+}  // namespace internal
+}  // namespace ceres