MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/unsymmetric_linear_solver_test.cc
2019-01-03 16:25:18 +08:00

252 lines
8.6 KiB
C++

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 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/compressed_row_sparse_matrix.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 {
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());
sol_unregularized_.reset(problem->x.release());
sol_regularized_.reset(problem->x_D.release());
}
void TestSolver(const LinearSolver::Options& 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());
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 = new CompressedRowSparseMatrix(*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);
}
transformed_A.reset(crsm);
} else {
LOG(FATAL) << "Unknown linear solver : " << options.type;
}
// Unregularized
scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
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());
EXPECT_EQ(unregularized_solve_summary.termination_type,
LINEAR_SOLVER_SUCCESS);
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()
<< "\nActual: " << x_unregularized.transpose();
}
EXPECT_EQ(regularized_solve_summary.termination_type,
LINEAR_SOLVER_SUCCESS);
for (int i = 0; i < A_->num_cols(); ++i) {
EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8)
<< "\nExpected: "
<< ConstVectorRef(sol_regularized_.get(), A_->num_cols()).transpose()
<< "\nActual: " << x_regularized.transpose();
}
}
scoped_ptr<TripletSparseMatrix> A_;
scoped_array<double> b_;
scoped_array<double> D_;
scoped_array<double> sol_unregularized_;
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,
SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = false;
TestSolver(options);
}
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = true;
TestSolver(options);
}
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingSuiteSparseDynamicSparsity) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.dynamic_sparsity = true;
TestSolver(options);
}
#endif
#ifndef CERES_NO_CXSPARSE
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingCXSparsePreOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = CX_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = false;
TestSolver(options);
}
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingCXSparsePostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = CX_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = true;
TestSolver(options);
}
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingCXSparseDynamicSparsity) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = CX_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.dynamic_sparsity = true;
TestSolver(options);
}
#endif
#ifdef CERES_USE_EIGEN_SPARSE
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingEigenPreOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = false;
TestSolver(options);
}
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingEigenPostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = true;
TestSolver(options);
}
TEST_F(UnsymmetricLinearSolverTest,
SparseNormalCholeskyUsingEigenDynamicSparsity) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.dynamic_sparsity = true;
TestSolver(options);
}
#endif // CERES_USE_EIGEN_SPARSE
} // namespace internal
} // namespace ceres