Initial commit of Ceres Solver.
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 <glog/logging.h>
+#include "gtest/gtest.h"
+#include "ceres/casts.h"
+#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/linear_least_squares_problems.h"
+#include "ceres/linear_solver.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/types.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());
+ sol1_.reset(problem->x.release());
+ sol2_.reset(problem->x_D.release());
+ x_.reset(new double[A_->num_cols()]);
+ }
+
+ void TestSolver(LinearSolverType linear_solver_type) {
+ LinearSolver::Options options;
+ options.type = linear_solver_type;
+ scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
+
+ LinearSolver::PerSolveOptions per_solve_options;
+
+ // Unregularized
+ LinearSolver::Summary summary =
+ solver->Solve(A_.get(), b_.get(), per_solve_options, x_.get());
+
+ EXPECT_EQ(summary.termination_type, TOLERANCE);
+
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ EXPECT_NEAR(sol1_[i], x_[i], 1e-8);
+ }
+
+ // Regularized solution
+ per_solve_options.D = D_.get();
+ summary = solver->Solve(A_.get(), b_.get(), per_solve_options, x_.get());
+
+ EXPECT_EQ(summary.termination_type, TOLERANCE);
+
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ EXPECT_NEAR(sol2_[i], x_[i], 1e-8);
+ }
+ }
+
+ scoped_ptr<TripletSparseMatrix> A_;
+ scoped_array<double> b_;
+ scoped_array<double> D_;
+ scoped_array<double> sol1_;
+ scoped_array<double> sol2_;
+
+ scoped_array<double> x_;
+};
+
+// TODO(keir): Reduce duplication.
+TEST_F(UnsymmetricLinearSolverTest, DenseQR) {
+ LinearSolver::Options options;
+ options.type = DENSE_QR;
+ scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
+
+ LinearSolver::PerSolveOptions per_solve_options;
+ DenseSparseMatrix A(*A_);
+
+ // Unregularized
+ LinearSolver::Summary summary =
+ solver->Solve(&A, b_.get(), per_solve_options, x_.get());
+
+ EXPECT_EQ(summary.termination_type, TOLERANCE);
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ EXPECT_NEAR(sol1_[i], x_[i], 1e-8);
+ }
+
+ VectorRef x(x_.get(), A_->num_cols());
+ VectorRef b(b_.get(), A_->num_rows());
+ Vector r = A.matrix()*x - b;
+ LOG(INFO) << "r = A*x - b: \n" << r;
+
+ // Regularized solution
+ per_solve_options.D = D_.get();
+ summary = solver->Solve(&A, b_.get(), per_solve_options, x_.get());
+
+ EXPECT_EQ(summary.termination_type, TOLERANCE);
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ EXPECT_NEAR(sol2_[i], x_[i], 1e-8);
+ }
+}
+
+#ifndef CERES_NO_SUITESPARSE
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholesky) {
+ LinearSolver::Options options;
+ options.type = SPARSE_NORMAL_CHOLESKY;
+ scoped_ptr<LinearSolver>solver(LinearSolver::Create(options));
+
+ LinearSolver::PerSolveOptions per_solve_options;
+ CompressedRowSparseMatrix A(*A_);
+
+ // Unregularized
+ LinearSolver::Summary summary =
+ solver->Solve(&A, b_.get(), per_solve_options, x_.get());
+
+ EXPECT_EQ(summary.termination_type, TOLERANCE);
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ EXPECT_NEAR(sol1_[i], x_[i], 1e-8);
+ }
+
+ // Regularized solution
+ per_solve_options.D = D_.get();
+ summary = solver->Solve(&A, b_.get(), per_solve_options, x_.get());
+
+ EXPECT_EQ(summary.termination_type, TOLERANCE);
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ EXPECT_NEAR(sol2_[i], x_[i], 1e-8);
+ }
+}
+#endif // CERES_NO_SUITESPARSE
+
+} // namespace internal
+} // namespace ceres
