Initial commit of Ceres Solver.
diff --git a/internal/ceres/normal_prior_test.cc b/internal/ceres/normal_prior_test.cc
new file mode 100644
index 0000000..67af881
--- /dev/null
+++ b/internal/ceres/normal_prior_test.cc
@@ -0,0 +1,133 @@
+// 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/normal_prior.h"
+
+#include <cstddef>
+
+#include "gtest/gtest.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/random.h"
+
+namespace ceres {
+namespace internal {
+
+void RandomVector(Vector* v) {
+ for (int r = 0; r < v->rows(); ++r)
+ (*v)[r] = 2 * RandDouble() - 1;
+}
+
+void RandomMatrix(Matrix* m) {
+ for (int r = 0; r < m->rows(); ++r) {
+ for (int c = 0; c < m->cols(); ++c) {
+ (*m)(r, c) = 2 * RandDouble() - 1;
+ }
+ }
+}
+
+TEST(NormalPriorTest, ResidualAtRandomPosition) {
+ srand(5);
+
+ for (int num_rows = 1; num_rows < 5; ++num_rows) {
+ for (int num_cols = 1; num_cols < 5; ++num_cols) {
+ Vector b(num_cols);
+ RandomVector(&b);
+
+ Matrix A(num_rows, num_cols);
+ RandomMatrix(&A);
+
+ double * x = new double[num_cols];
+ for (int i = 0; i < num_cols; ++i)
+ x[i] = 2 * RandDouble() - 1;
+
+ double * jacobian = new double[num_rows * num_cols];
+ Vector residuals(num_rows);
+
+ NormalPrior prior(A, b);
+ prior.Evaluate(&x, residuals.data(), &jacobian);
+
+ // Compare the norm of the residual
+ double residual_diff_norm =
+ (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
+ EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
+
+ // Compare the jacobians
+ MatrixRef J(jacobian, num_rows, num_cols);
+ double jacobian_diff_norm = (J - A).norm();
+ EXPECT_NEAR(jacobian_diff_norm, 0.0, 1e-10);
+
+ delete []x;
+ delete []jacobian;
+ }
+ }
+}
+
+TEST(NormalPriorTest, ResidualAtRandomPositionNullJacobians) {
+ srand(5);
+
+ for (int num_rows = 1; num_rows < 5; ++num_rows) {
+ for (int num_cols = 1; num_cols < 5; ++num_cols) {
+ Vector b(num_cols);
+ RandomVector(&b);
+
+ Matrix A(num_rows, num_cols);
+ RandomMatrix(&A);
+
+ double * x = new double[num_cols];
+ for (int i = 0; i < num_cols; ++i)
+ x[i] = 2 * RandDouble() - 1;
+
+ double* jacobians[1];
+ jacobians[0] = NULL;
+
+ Vector residuals(num_rows);
+
+ NormalPrior prior(A, b);
+ prior.Evaluate(&x, residuals.data(), jacobians);
+
+ // Compare the norm of the residual
+ double residual_diff_norm =
+ (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
+ EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
+
+ prior.Evaluate(&x, residuals.data(), NULL);
+ // Compare the norm of the residual
+ residual_diff_norm =
+ (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
+ EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
+
+
+ delete []x;
+ }
+ }
+}
+
+} // namespace internal
+} // namespace ceres
