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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
// 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 <memory>
#include "ceres/casts.h"
#include "ceres/context_impl.h"
#include "ceres/internal/config.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 {
using Param = ::testing::
tuple<LinearSolverType, DenseLinearAlgebraLibraryType, bool, int>;
static 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);
std::unique_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);
ContextImpl context;
options.context = &context;
std::unique_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);
Vector normal_rhs = lhs.matrix().transpose() * rhs.head(num_rows);
Matrix normal_lhs = lhs.matrix().transpose() * lhs.matrix();
if (regularized) {
ConstVectorRef diagonal(problem->D.get(), num_cols);
normal_lhs += diagonal.array().square().matrix().asDiagonal();
}
Vector actual_normal_rhs = normal_lhs * solution;
const double normalized_residual =
(normal_rhs - actual_normal_rhs).norm() / normal_rhs.norm();
EXPECT_NEAR(
normalized_residual, 0.0, 10 * std::numeric_limits<double>::epsilon())
<< "\nexpected: " << normal_rhs.transpose()
<< "\nactual: " << actual_normal_rhs.transpose();
}
namespace {
// TODO(sameeragarwal): Should we move away from hard coded linear
// least squares problem to randomly generated ones?
#ifndef CERES_NO_LAPACK
INSTANTIATE_TEST_SUITE_P(
DenseLinearSolver,
DenseLinearSolverTest,
::testing::Combine(::testing::Values(DENSE_QR, DENSE_NORMAL_CHOLESKY),
::testing::Values(EIGEN, LAPACK),
::testing::Values(true, false),
::testing::Values(0, 1)),
ParamInfoToString);
#else
INSTANTIATE_TEST_SUITE_P(
DenseLinearSolver,
DenseLinearSolverTest,
::testing::Combine(::testing::Values(DENSE_QR, DENSE_NORMAL_CHOLESKY),
::testing::Values(EIGEN),
::testing::Values(true, false),
::testing::Values(0, 1)),
ParamInfoToString);
#endif
} // namespace
} // namespace internal
} // namespace ceres
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