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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// 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
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/dense_qr.h"
#include <memory>
#include <numeric>
#include <string>
#include <tuple>
#include <vector>
#include "Eigen/Dense"
#include "ceres/internal/eigen.h"
#include "ceres/linear_solver.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
namespace ceres::internal {
using Param = DenseLinearAlgebraLibraryType;
namespace {
std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
return DenseLinearAlgebraLibraryTypeToString(info.param);
}
} // namespace
class DenseQRTest : public ::testing::TestWithParam<Param> {};
TEST_P(DenseQRTest, FactorAndSolve) {
// TODO(sameeragarwal): Convert these tests into type parameterized tests so
// that we can test the single and double precision solvers.
using Scalar = double;
using MatrixType = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>;
using VectorType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
LinearSolver::Options options;
ContextImpl context;
#ifndef CERES_NO_CUDA
options.context = &context;
std::string error;
ASSERT_TRUE(context.InitCuda(&error)) << error;
#endif // CERES_NO_CUDA
options.dense_linear_algebra_library_type = GetParam();
const double kEpsilon = std::numeric_limits<double>::epsilon() * 1.5e4;
std::unique_ptr<DenseQR> dense_qr = DenseQR::Create(options);
const int kNumTrials = 10;
const int kMinNumCols = 1;
const int kMaxNumCols = 10;
const int kMinRowsFactor = 1;
const int kMaxRowsFactor = 3;
for (int num_cols = kMinNumCols; num_cols < kMaxNumCols; ++num_cols) {
for (int num_rows = kMinRowsFactor * num_cols;
num_rows < kMaxRowsFactor * num_cols;
++num_rows) {
for (int trial = 0; trial < kNumTrials; ++trial) {
MatrixType lhs = MatrixType::Random(num_rows, num_cols);
Vector x = VectorType::Random(num_cols);
Vector rhs = lhs * x;
Vector actual = Vector::Random(num_cols);
LinearSolver::Summary summary;
summary.termination_type = dense_qr->FactorAndSolve(num_rows,
num_cols,
lhs.data(),
rhs.data(),
actual.data(),
&summary.message);
ASSERT_EQ(summary.termination_type,
LinearSolverTerminationType::SUCCESS);
ASSERT_NEAR((x - actual).norm() / x.norm(), 0.0, kEpsilon)
<< "\nexpected: " << x.transpose()
<< "\nactual : " << actual.transpose();
}
}
}
}
namespace {
// NOTE: preprocessor directives in a macro are not standard conforming
decltype(auto) MakeValues() {
return ::testing::Values(EIGEN
#ifndef CERES_NO_LAPACK
,
LAPACK
#endif
#ifndef CERES_NO_CUDA
,
CUDA
#endif
);
}
} // namespace
INSTANTIATE_TEST_SUITE_P(_, DenseQRTest, MakeValues(), ParamInfoToString);
} // namespace ceres::internal