Refactor unsymmetric_linear_solver_test
1. Break up unsymmetric_linear_solver_test into
a. dense_linear_solver_test which covers DENSE_QR and
DENSE_NORMAL_CHOLESKY.
b. sparse_normal_cholesky_solver_test which covers
SPARSE_NORMAL_CHOLESKY.
2. dense_linear_solver_test has been completely re-written. It now
uses value parameterized tests for better logging. The number of
test problems as been increased to 2. Last but not the least
the actual test of correctness is not based on a golden solution
computed using another linear solver. We now compute the residual
and ensure that it is small.
https://github.com/ceres-solver/ceres-solver/issues/279
Change-Id: I9546a43e8ae85c31b2096a99405e47da326755ee
diff --git a/internal/ceres/CMakeLists.txt b/internal/ceres/CMakeLists.txt
index cb284b0..e4dca24 100644
--- a/internal/ceres/CMakeLists.txt
+++ b/internal/ceres/CMakeLists.txt
@@ -299,6 +299,7 @@
ceres_test(covariance)
ceres_test(cubic_interpolation)
ceres_test(detect_structure)
+ ceres_test(dense_linear_solver)
ceres_test(dense_sparse_matrix)
ceres_test(dynamic_autodiff_cost_function)
ceres_test(dynamic_compressed_row_sparse_matrix)
@@ -343,6 +344,7 @@
ceres_test(small_blas)
ceres_test(solver)
ceres_test(sparse_cholesky)
+ ceres_test(sparse_normal_cholesky_solver)
# TODO(sameeragarwal): This test should ultimately be made
# independent of SuiteSparse.
@@ -356,7 +358,6 @@
ceres_test(triplet_sparse_matrix)
ceres_test(trust_region_minimizer)
ceres_test(trust_region_preprocessor)
- ceres_test(unsymmetric_linear_solver)
ceres_test(visibility)
ceres_test(visibility_based_preconditioner)
diff --git a/internal/ceres/dense_linear_solver_test.cc b/internal/ceres/dense_linear_solver_test.cc
new file mode 100644
index 0000000..770a2d3
--- /dev/null
+++ b/internal/ceres/dense_linear_solver_test.cc
@@ -0,0 +1,123 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2017 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// 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/casts.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/linear_least_squares_problems.h"
+#include "ceres/linear_solver.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/types.h"
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+typedef ::testing::
+ tuple<LinearSolverType, DenseLinearAlgebraLibraryType, bool, int>
+ Param;
+
+std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
+ Param param = info.param;
+ std::stringstream ss;
+ ss << LinearSolverTypeToString(::testing::get<0>(param)) << "_"
+ << DenseLinearAlgebraLibraryTypeToString(::testing::get<1>(param)) << "_"
+ << (::testing::get<2>(param) ? "Regularized" : "Unregularized") << "_"
+ << ::testing::get<3>(param);
+ return ss.str();
+}
+
+class DenseLinearSolverTest : public ::testing::TestWithParam<Param> {};
+
+TEST_P(DenseLinearSolverTest, _) {
+ Param param = GetParam();
+ const bool regularized = testing::get<2>(param);
+
+ scoped_ptr<LinearLeastSquaresProblem> problem(
+ CreateLinearLeastSquaresProblemFromId(testing::get<3>(param)));
+ DenseSparseMatrix lhs(*down_cast<TripletSparseMatrix*>(problem->A.get()));
+
+ const int num_cols = lhs.num_cols();
+ const int num_rows = lhs.num_rows();
+
+ Vector rhs = Vector::Zero(num_rows + num_cols);
+ rhs.head(num_rows) = ConstVectorRef(problem->b.get(), num_rows);
+
+ LinearSolver::Options options;
+ options.type = ::testing::get<0>(param);
+ options.dense_linear_algebra_library_type = ::testing::get<1>(param);
+ scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
+
+ LinearSolver::PerSolveOptions per_solve_options;
+ if (regularized) {
+ per_solve_options.D = problem->D.get();
+ }
+
+ Vector solution(num_cols);
+ LinearSolver::Summary summary =
+ solver->Solve(&lhs, rhs.data(), per_solve_options, solution.data());
+ EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_SUCCESS);
+
+ // If solving for the regularized solution, add the diagonal to the
+ // matrix. This makes subsequent computations simpler.
+ if (testing::get<2>(param)) {
+ lhs.AppendDiagonal(problem->D.get());
+ };
+
+ Vector tmp = Vector::Zero(num_rows + num_cols);
+ lhs.RightMultiply(solution.data(), tmp.data());
+ Vector actual_normal_rhs = Vector::Zero(num_cols);
+ lhs.LeftMultiply(tmp.data(), actual_normal_rhs.data());
+
+ Vector expected_normal_rhs = Vector::Zero(num_cols);
+ lhs.LeftMultiply(rhs.data(), expected_normal_rhs.data());
+ const double residual = (expected_normal_rhs - actual_normal_rhs).norm() /
+ expected_normal_rhs.norm();
+
+ EXPECT_NEAR(residual, 0.0, 10 * std::numeric_limits<double>::epsilon());
+}
+
+// TODO(sameeragarwal): Should we move away from hard coded linear
+// least squares problem to randomly generated ones?
+INSTANTIATE_TEST_CASE_P(
+ DenseLinearSolver,
+ DenseLinearSolverTest,
+ ::testing::Combine(::testing::Values(DENSE_QR, DENSE_NORMAL_CHOLESKY),
+#ifdef CERES_NO_LAPACK
+ ::testing::Values(EIGEN),
+#else
+ ::testing::Values(EIGEN, LAPACK),
+#endif
+ ::testing::Values(true, false),
+ ::testing::Values(0, 1)),
+ ParamInfoToString);
+
+} // namespace internal
+} // namespace ceres
diff --git a/internal/ceres/unsymmetric_linear_solver_test.cc b/internal/ceres/sparse_normal_cholesky_solver_test.cc
similarity index 68%
rename from internal/ceres/unsymmetric_linear_solver_test.cc
rename to internal/ceres/sparse_normal_cholesky_solver_test.cc
index 401db1e..c4a1c69 100644
--- a/internal/ceres/unsymmetric_linear_solver_test.cc
+++ b/internal/ceres/sparse_normal_cholesky_solver_test.cc
@@ -1,5 +1,5 @@
// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2015 Google Inc. All rights reserved.
+// Copyright 2017 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
@@ -41,9 +41,15 @@
namespace ceres {
namespace internal {
-// TODO(sameeragarwal): Refactor and expand these tests.
-class UnsymmetricLinearSolverTest : public ::testing::Test {
- protected :
+// TODO(sameeragarwal): This tests needs to be re-written, since
+// SparseNormalCholeskySolver is a composition of two classes now,
+// OuterProduct and SparseCholesky.
+//
+// So the test should exercise the composition, rather than the
+// numerics of the solver, which are well covered by tests for those
+// classes.
+class SparseNormalCholeskyLinearSolverTest : public ::testing::Test {
+ protected:
virtual void SetUp() {
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(0));
@@ -65,51 +71,42 @@
scoped_ptr<SparseMatrix> transformed_A;
- if (options.type == DENSE_QR ||
- options.type == DENSE_NORMAL_CHOLESKY) {
- transformed_A.reset(new DenseSparseMatrix(*A_));
- } else if (options.type == SPARSE_NORMAL_CHOLESKY) {
- CompressedRowSparseMatrix* crsm =
- CompressedRowSparseMatrix::FromTripletSparseMatrix(*A_);
- // Add row/column blocks structure.
- for (int i = 0; i < A_->num_rows(); ++i) {
- crsm->mutable_row_blocks()->push_back(1);
- }
-
- for (int i = 0; i < A_->num_cols(); ++i) {
- crsm->mutable_col_blocks()->push_back(1);
- }
-
- // With all blocks of size 1, crsb_rows and crsb_cols are equivalent to
- // rows and cols.
- std::copy(crsm->rows(), crsm->rows() + crsm->num_rows() + 1,
- std::back_inserter(*crsm->mutable_crsb_rows()));
-
- std::copy(crsm->cols(), crsm->cols() + crsm->num_nonzeros(),
- std::back_inserter(*crsm->mutable_crsb_cols()));
-
- transformed_A.reset(crsm);
- } else {
- LOG(FATAL) << "Unknown linear solver : " << options.type;
+ CompressedRowSparseMatrix* crsm =
+ CompressedRowSparseMatrix::FromTripletSparseMatrix(*A_);
+ // Add row/column blocks structure.
+ for (int i = 0; i < A_->num_rows(); ++i) {
+ crsm->mutable_row_blocks()->push_back(1);
}
+ for (int i = 0; i < A_->num_cols(); ++i) {
+ crsm->mutable_col_blocks()->push_back(1);
+ }
+
+ // With all blocks of size 1, crsb_rows and crsb_cols are equivalent to
+ // rows and cols.
+ std::copy(crsm->rows(),
+ crsm->rows() + crsm->num_rows() + 1,
+ std::back_inserter(*crsm->mutable_crsb_rows()));
+
+ std::copy(crsm->cols(),
+ crsm->cols() + crsm->num_nonzeros(),
+ std::back_inserter(*crsm->mutable_crsb_cols()));
+
+ transformed_A.reset(crsm);
+
// Unregularized
scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
- unregularized_solve_summary =
- solver->Solve(transformed_A.get(),
- b_.get(),
- per_solve_options,
- x_unregularized.data());
+ unregularized_solve_summary = solver->Solve(transformed_A.get(),
+ b_.get(),
+ per_solve_options,
+ x_unregularized.data());
// Sparsity structure is changing, reset the solver.
solver.reset(LinearSolver::Create(options));
// Regularized solution
per_solve_options.D = D_.get();
- regularized_solve_summary =
- solver->Solve(transformed_A.get(),
- b_.get(),
- per_solve_options,
- x_regularized.data());
+ regularized_solve_summary = solver->Solve(
+ transformed_A.get(), b_.get(), per_solve_options, x_regularized.data());
EXPECT_EQ(unregularized_solve_summary.termination_type,
LINEAR_SOLVER_SUCCESS);
@@ -117,8 +114,8 @@
for (int i = 0; i < A_->num_cols(); ++i) {
EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8)
<< "\nExpected: "
- << ConstVectorRef(sol_unregularized_.get(),
- A_->num_cols()).transpose()
+ << ConstVectorRef(sol_unregularized_.get(), A_->num_cols())
+ .transpose()
<< "\nActual: " << x_unregularized.transpose();
}
@@ -139,38 +136,8 @@
scoped_array<double> sol_regularized_;
};
-TEST_F(UnsymmetricLinearSolverTest, EigenDenseQR) {
- LinearSolver::Options options;
- options.type = DENSE_QR;
- options.dense_linear_algebra_library_type = EIGEN;
- TestSolver(options);
-}
-
-TEST_F(UnsymmetricLinearSolverTest, EigenDenseNormalCholesky) {
- LinearSolver::Options options;
- options.dense_linear_algebra_library_type = EIGEN;
- options.type = DENSE_NORMAL_CHOLESKY;
- TestSolver(options);
-}
-
-#ifndef CERES_NO_LAPACK
-TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseQR) {
- LinearSolver::Options options;
- options.type = DENSE_QR;
- options.dense_linear_algebra_library_type = LAPACK;
- TestSolver(options);
-}
-
-TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseNormalCholesky) {
- LinearSolver::Options options;
- options.dense_linear_algebra_library_type = LAPACK;
- options.type = DENSE_NORMAL_CHOLESKY;
- TestSolver(options);
-}
-#endif
-
#ifndef CERES_NO_SUITESPARSE
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
@@ -179,7 +146,7 @@
TestSolver(options);
}
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
@@ -188,7 +155,7 @@
TestSolver(options);
}
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingSuiteSparseDynamicSparsity) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
@@ -199,7 +166,7 @@
#endif
#ifndef CERES_NO_CXSPARSE
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingCXSparsePreOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = CX_SPARSE;
@@ -208,7 +175,7 @@
TestSolver(options);
}
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingCXSparsePostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = CX_SPARSE;
@@ -217,7 +184,7 @@
TestSolver(options);
}
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingCXSparseDynamicSparsity) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = CX_SPARSE;
@@ -228,7 +195,7 @@
#endif
#ifdef CERES_USE_EIGEN_SPARSE
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingEigenPreOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
@@ -237,7 +204,7 @@
TestSolver(options);
}
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingEigenPostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
@@ -246,7 +213,7 @@
TestSolver(options);
}
-TEST_F(UnsymmetricLinearSolverTest,
+TEST_F(SparseNormalCholeskyLinearSolverTest,
SparseNormalCholeskyUsingEigenDynamicSparsity) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
