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// 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
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// 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/block_sparse_matrix.h"
#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"
#include "Eigen/Cholesky"
namespace ceres {
namespace internal {
// TODO(sameeragarwal): These tests needs to be re-written, since
// SparseNormalCholeskySolver is a composition of two classes now,
// InnerProductComputer 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 SparseNormalCholeskySolverTest : public ::testing::Test {
protected:
virtual void SetUp() {
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(2));
CHECK_NOTNULL(problem.get());
A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
b_.reset(problem->b.release());
D_.reset(problem->D.release());
}
void TestSolver(const LinearSolver::Options& options, double* D) {
Matrix dense_A;
A_->ToDenseMatrix(&dense_A);
Matrix lhs = dense_A.transpose() * dense_A;
if (D != NULL) {
lhs += (ConstVectorRef(D, A_->num_cols()).array() *
ConstVectorRef(D, A_->num_cols()).array())
.matrix()
.asDiagonal();
}
Vector rhs(A_->num_cols());
rhs.setZero();
A_->LeftMultiply(b_.get(), rhs.data());
Vector expected_solution = lhs.llt().solve(rhs);
scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
LinearSolver::PerSolveOptions per_solve_options;
per_solve_options.D = D;
Vector actual_solution(A_->num_cols());
LinearSolver::Summary summary;
summary = solver->Solve(
A_.get(), b_.get(), per_solve_options, actual_solution.data());
EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_SUCCESS);
for (int i = 0; i < A_->num_cols(); ++i) {
EXPECT_NEAR(expected_solution(i), actual_solution(i), 1e-8)
<< "\nExpected: " << expected_solution.transpose()
<< "\nActual: " << actual_solution.transpose();
}
}
void TestSolver(const LinearSolver::Options& options) {
TestSolver(options, NULL);
TestSolver(options, D_.get());
}
scoped_ptr<BlockSparseMatrix> A_;
scoped_array<double> b_;
scoped_array<double> D_;
};
#ifndef CERES_NO_SUITESPARSE
TEST_F(SparseNormalCholeskySolverTest,
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(SparseNormalCholeskySolverTest,
SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = true;
TestSolver(options);
}
#endif
#ifndef CERES_NO_CXSPARSE
TEST_F(SparseNormalCholeskySolverTest,
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(SparseNormalCholeskySolverTest,
SparseNormalCholeskyUsingCXSparsePostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = CX_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = true;
TestSolver(options);
}
#endif
#ifdef CERES_USE_EIGEN_SPARSE
TEST_F(SparseNormalCholeskySolverTest,
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(SparseNormalCholeskySolverTest,
SparseNormalCholeskyUsingEigenPostOrdering) {
LinearSolver::Options options;
options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
options.type = SPARSE_NORMAL_CHOLESKY;
options.use_postordering = true;
TestSolver(options);
}
#endif // CERES_USE_EIGEN_SPARSE
} // namespace internal
} // namespace ceres