diff --git a/internal/ceres/small_blas_test.cc b/internal/ceres/small_blas_test.cc
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+++ b/internal/ceres/small_blas_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: keir@google.com (Keir Mierle)
+
+#include "ceres/small_blas.h"
+
+#include "gtest/gtest.h"
+#include "ceres/internal/eigen.h"
+
+namespace ceres {
+namespace internal {
+
+TEST(BLAS, MatrixMatrixMultiply) {
+  const double kTolerance = 1e-16;
+  const int kRowA = 3;
+  const int kColA = 5;
+  Matrix A(kRowA, kColA);
+  A.setOnes();
+
+  const int kRowB = 5;
+  const int kColB = 7;
+  Matrix B(kRowB, kColB);
+  B.setOnes();
+
+  for (int row_stride_c = kRowA; row_stride_c < 3 * kRowA; ++row_stride_c) {
+    for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {
+      Matrix C(row_stride_c, col_stride_c);
+      C.setOnes();
+
+      Matrix C_plus = C;
+      Matrix C_minus = C;
+      Matrix C_assign = C;
+
+      Matrix C_plus_ref = C;
+      Matrix C_minus_ref = C;
+      Matrix C_assign_ref = C;
+      for (int start_row_c = 0; start_row_c + kRowA < row_stride_c; ++start_row_c) {
+        for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
+          C_plus_ref.block(start_row_c, start_col_c, kRowA, kColB) +=
+              A * B;
+
+          MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(
+              A.data(), kRowA, kColA,
+              B.data(), kRowB, kColB,
+              C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+          EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
+              << "C += A * B \n"
+              << "row_stride_c : " << row_stride_c << "\n"
+              << "col_stride_c : " << col_stride_c << "\n"
+              << "start_row_c  : " << start_row_c << "\n"
+              << "start_col_c  : " << start_col_c << "\n"
+              << "Cref : \n" << C_plus_ref << "\n"
+              << "C: \n" << C_plus;
+
+
+          C_minus_ref.block(start_row_c, start_col_c, kRowA, kColB) -=
+              A * B;
+
+          MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(
+              A.data(), kRowA, kColA,
+              B.data(), kRowB, kColB,
+              C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+           EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
+              << "C -= A * B \n"
+              << "row_stride_c : " << row_stride_c << "\n"
+              << "col_stride_c : " << col_stride_c << "\n"
+              << "start_row_c  : " << start_row_c << "\n"
+              << "start_col_c  : " << start_col_c << "\n"
+              << "Cref : \n" << C_minus_ref << "\n"
+              << "C: \n" << C_minus;
+
+          C_assign_ref.block(start_row_c, start_col_c, kRowA, kColB) =
+              A * B;
+
+          MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(
+              A.data(), kRowA, kColA,
+              B.data(), kRowB, kColB,
+              C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+          EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
+              << "C = A * B \n"
+              << "row_stride_c : " << row_stride_c << "\n"
+              << "col_stride_c : " << col_stride_c << "\n"
+              << "start_row_c  : " << start_row_c << "\n"
+              << "start_col_c  : " << start_col_c << "\n"
+              << "Cref : \n" << C_assign_ref << "\n"
+              << "C: \n" << C_assign;
+        }
+      }
+    }
+  }
+}
+
+TEST(BLAS, MatrixTransposeMatrixMultiply) {
+  const double kTolerance = 1e-16;
+  const int kRowA = 5;
+  const int kColA = 3;
+  Matrix A(kRowA, kColA);
+  A.setOnes();
+
+  const int kRowB = 5;
+  const int kColB = 7;
+  Matrix B(kRowB, kColB);
+  B.setOnes();
+
+  for (int row_stride_c = kColA; row_stride_c < 3 * kColA; ++row_stride_c) {
+    for (int col_stride_c = kColB; col_stride_c <  3 * kColB; ++col_stride_c) {
+      Matrix C(row_stride_c, col_stride_c);
+      C.setOnes();
+
+      Matrix C_plus = C;
+      Matrix C_minus = C;
+      Matrix C_assign = C;
+
+      Matrix C_plus_ref = C;
+      Matrix C_minus_ref = C;
+      Matrix C_assign_ref = C;
+      for (int start_row_c = 0; start_row_c + kColA < row_stride_c; ++start_row_c) {
+        for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
+          C_plus_ref.block(start_row_c, start_col_c, kColA, kColB) +=
+              A.transpose() * B;
+
+          MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(
+              A.data(), kRowA, kColA,
+              B.data(), kRowB, kColB,
+              C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+          EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
+              << "C += A' * B \n"
+              << "row_stride_c : " << row_stride_c << "\n"
+              << "col_stride_c : " << col_stride_c << "\n"
+              << "start_row_c  : " << start_row_c << "\n"
+              << "start_col_c  : " << start_col_c << "\n"
+              << "Cref : \n" << C_plus_ref << "\n"
+              << "C: \n" << C_plus;
+
+          C_minus_ref.block(start_row_c, start_col_c, kColA, kColB) -=
+              A.transpose() * B;
+
+          MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(
+              A.data(), kRowA, kColA,
+              B.data(), kRowB, kColB,
+              C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+          EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
+              << "C -= A' * B \n"
+              << "row_stride_c : " << row_stride_c << "\n"
+              << "col_stride_c : " << col_stride_c << "\n"
+              << "start_row_c  : " << start_row_c << "\n"
+              << "start_col_c  : " << start_col_c << "\n"
+              << "Cref : \n" << C_minus_ref << "\n"
+              << "C: \n" << C_minus;
+
+          C_assign_ref.block(start_row_c, start_col_c, kColA, kColB) =
+              A.transpose() * B;
+
+          MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(
+              A.data(), kRowA, kColA,
+              B.data(), kRowB, kColB,
+              C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+          EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
+              << "C = A' * B \n"
+              << "row_stride_c : " << row_stride_c << "\n"
+              << "col_stride_c : " << col_stride_c << "\n"
+              << "start_row_c  : " << start_row_c << "\n"
+              << "start_col_c  : " << start_col_c << "\n"
+              << "Cref : \n" << C_assign_ref << "\n"
+              << "C: \n" << C_assign;
+        }
+      }
+    }
+  }
+}
+
+TEST(BLAS, MatrixVectorMultiply) {
+  const double kTolerance = 1e-16;
+  const int kRowA = 5;
+  const int kColA = 3;
+  Matrix A(kRowA, kColA);
+  A.setOnes();
+
+  Vector b(kColA);
+  b.setOnes();
+
+  Vector c(kRowA);
+  c.setOnes();
+
+  Vector c_plus = c;
+  Vector c_minus = c;
+  Vector c_assign = c;
+
+  Vector c_plus_ref = c;
+  Vector c_minus_ref = c;
+  Vector c_assign_ref = c;
+
+  c_plus_ref += A * b;
+  MatrixVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
+                                        b.data(),
+                                        c_plus.data());
+  EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
+      << "c += A * b \n"
+      << "c_ref : \n" << c_plus_ref << "\n"
+      << "c: \n" << c_plus;
+
+  c_minus_ref -= A * b;
+  MatrixVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
+                                                 b.data(),
+                                                 c_minus.data());
+  EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
+      << "c += A * b \n"
+      << "c_ref : \n" << c_minus_ref << "\n"
+      << "c: \n" << c_minus;
+
+  c_assign_ref = A * b;
+  MatrixVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
+                                                  b.data(),
+                                                  c_assign.data());
+  EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
+      << "c += A * b \n"
+      << "c_ref : \n" << c_assign_ref << "\n"
+      << "c: \n" << c_assign;
+}
+
+TEST(BLAS, MatrixTransposeVectorMultiply) {
+  const double kTolerance = 1e-16;
+  const int kRowA = 5;
+  const int kColA = 3;
+  Matrix A(kRowA, kColA);
+  A.setRandom();
+
+  Vector b(kRowA);
+  b.setRandom();
+
+  Vector c(kColA);
+  c.setOnes();
+
+  Vector c_plus = c;
+  Vector c_minus = c;
+  Vector c_assign = c;
+
+  Vector c_plus_ref = c;
+  Vector c_minus_ref = c;
+  Vector c_assign_ref = c;
+
+  c_plus_ref += A.transpose() * b;
+  MatrixTransposeVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
+                                                 b.data(),
+                                                 c_plus.data());
+  EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
+      << "c += A' * b \n"
+      << "c_ref : \n" << c_plus_ref << "\n"
+      << "c: \n" << c_plus;
+
+  c_minus_ref -= A.transpose() * b;
+  MatrixTransposeVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
+                                                 b.data(),
+                                                 c_minus.data());
+  EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
+      << "c += A' * b \n"
+      << "c_ref : \n" << c_minus_ref << "\n"
+      << "c: \n" << c_minus;
+
+  c_assign_ref = A.transpose() * b;
+  MatrixTransposeVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
+                                                  b.data(),
+                                                  c_assign.data());
+  EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
+      << "c += A' * b \n"
+      << "c_ref : \n" << c_assign_ref << "\n"
+      << "c: \n" << c_assign;
+}
+
+}  // namespace internal
+}  // namespace ceres