Multiple sparse linear algebra backends.
1. Added support for CXSparse - SparseNormalCholesky and
SchurComplementSolver support SuiteSparse and CXSparse now.
I am not sure I will add suport for visibility based
preconditioning using CXSparse. Its not a high priority.
2. New enum SparseLinearAlgebraLibraryType which allows the user
to indicate which sparse linear algebra library should be used.
3. Updated tests for SolverImpl and system_test.
4. Build system changes to automatically detect CXSparse and
link to it by default -- just like SuiteSparse.
5. Minor bug fixes dealing in the cmake files and VBP.
6. Changed the order of the system test.
7. Deduped the unsymmetric linear solver test.
Change-Id: I33252a103c87b722ecb7ed7b5f0ae7fd91249244
diff --git a/internal/ceres/unsymmetric_linear_solver_test.cc b/internal/ceres/unsymmetric_linear_solver_test.cc
index be91056..a7ee23e 100644
--- a/internal/ceres/unsymmetric_linear_solver_test.cc
+++ b/internal/ceres/unsymmetric_linear_solver_test.cc
@@ -52,109 +52,82 @@
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()]);
+ sol_unregularized_.reset(problem->x.release());
+ sol_regularized_.reset(problem->x_D.release());
}
- void TestSolver(LinearSolverType linear_solver_type) {
+ void TestSolver(
+ LinearSolverType linear_solver_type,
+ SparseLinearAlgebraLibraryType sparse_linear_algebra_library) {
LinearSolver::Options options;
options.type = linear_solver_type;
+ options.sparse_linear_algebra_library = sparse_linear_algebra_library;
scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
LinearSolver::PerSolveOptions per_solve_options;
+ LinearSolver::Summary unregularized_solve_summary;
+ LinearSolver::Summary regularized_solve_summary;
+ Vector x_unregularized(A_->num_cols());
+ Vector x_regularized(A_->num_cols());
- // Unregularized
- LinearSolver::Summary summary =
- solver->Solve(A_.get(), b_.get(), per_solve_options, x_.get());
+ scoped_ptr<SparseMatrix> transformed_A;
- EXPECT_EQ(summary.termination_type, TOLERANCE);
-
- for (int i = 0; i < A_->num_cols(); ++i) {
- EXPECT_NEAR(sol1_[i], x_[i], 1e-8);
+ if (linear_solver_type == DENSE_QR) {
+ transformed_A.reset(new DenseSparseMatrix(*A_));
+ } else if (linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
+ transformed_A.reset(new CompressedRowSparseMatrix(*A_));
+ } else {
+ LOG(FATAL) << "Unknown linear solver : " << linear_solver_type;
}
+ // Unregularized
+ unregularized_solve_summary =
+ solver->Solve(transformed_A.get(),
+ b_.get(),
+ per_solve_options,
+ x_unregularized.data());
// Regularized solution
per_solve_options.D = D_.get();
- summary = solver->Solve(A_.get(), b_.get(), per_solve_options, x_.get());
+ regularized_solve_summary =
+ solver->Solve(transformed_A.get(),
+ b_.get(),
+ per_solve_options,
+ x_regularized.data());
- EXPECT_EQ(summary.termination_type, TOLERANCE);
+ EXPECT_EQ(unregularized_solve_summary.termination_type, TOLERANCE);
for (int i = 0; i < A_->num_cols(); ++i) {
- EXPECT_NEAR(sol2_[i], x_[i], 1e-8);
+ EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8);
+ }
+
+ EXPECT_EQ(regularized_solve_summary.termination_type, TOLERANCE);
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ EXPECT_NEAR(sol_regularized_[i], x_regularized[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_;
+ scoped_array<double> sol_unregularized_;
+ scoped_array<double> sol_regularized_;
};
-// 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);
- }
+ TestSolver(DENSE_QR, SUITE_SPARSE);
}
#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);
- }
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingSuiteSparse) {
+ TestSolver(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE);
}
-#endif // CERES_NO_SUITESPARSE
+#endif
+
+#ifndef CERES_NO_CXSPARSE
+TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingCXSparse) {
+ TestSolver(SPARSE_NORMAL_CHOLESKY, CX_SPARSE);
+}
+#endif
} // namespace internal
} // namespace ceres
