| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #include "main.h" |
| #include <Eigen/QR> |
| #include "solverbase.h" |
| |
| template <typename MatrixType> |
| void qr() { |
| static const int Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime; |
| Index max_size = EIGEN_TEST_MAX_SIZE; |
| Index min_size = numext::maxi(1, EIGEN_TEST_MAX_SIZE / 10); |
| Index rows = Rows == Dynamic ? internal::random<Index>(min_size, max_size) : Rows, |
| cols = Cols == Dynamic ? internal::random<Index>(min_size, max_size) : Cols, |
| cols2 = Cols == Dynamic ? internal::random<Index>(min_size, max_size) : Cols, |
| rank = internal::random<Index>(1, (std::min)(rows, cols) - 1); |
| |
| typedef typename MatrixType::Scalar Scalar; |
| typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType; |
| MatrixType m1; |
| createRandomPIMatrixOfRank(rank, rows, cols, m1); |
| FullPivHouseholderQR<MatrixType> qr(m1); |
| VERIFY_IS_EQUAL(rank, qr.rank()); |
| VERIFY_IS_EQUAL(cols - qr.rank(), qr.dimensionOfKernel()); |
| VERIFY(!qr.isInjective()); |
| VERIFY(!qr.isInvertible()); |
| VERIFY(!qr.isSurjective()); |
| |
| MatrixType r = qr.matrixQR(); |
| |
| MatrixQType q = qr.matrixQ(); |
| VERIFY_IS_UNITARY(q); |
| |
| // FIXME need better way to construct trapezoid |
| for (int i = 0; i < rows; i++) |
| for (int j = 0; j < cols; j++) |
| if (i > j) r(i, j) = Scalar(0); |
| |
| MatrixType c = qr.matrixQ() * r * qr.colsPermutation().inverse(); |
| |
| VERIFY_IS_APPROX(m1, c); |
| |
| // stress the ReturnByValue mechanism |
| MatrixType tmp; |
| VERIFY_IS_APPROX(tmp.noalias() = qr.matrixQ() * r, (qr.matrixQ() * r).eval()); |
| |
| check_solverbase<MatrixType, MatrixType>(m1, qr, rows, cols, cols2); |
| |
| { |
| MatrixType m2, m3; |
| Index size = rows; |
| do { |
| m1 = MatrixType::Random(size, size); |
| qr.compute(m1); |
| } while (!qr.isInvertible()); |
| MatrixType m1_inv = qr.inverse(); |
| m3 = m1 * MatrixType::Random(size, cols2); |
| m2 = qr.solve(m3); |
| VERIFY_IS_APPROX(m2, m1_inv * m3); |
| } |
| } |
| |
| template <typename MatrixType> |
| void qr_invertible() { |
| using std::abs; |
| using std::log; |
| typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; |
| typedef typename MatrixType::Scalar Scalar; |
| |
| Index max_size = numext::mini(50, EIGEN_TEST_MAX_SIZE); |
| Index min_size = numext::maxi(1, EIGEN_TEST_MAX_SIZE / 10); |
| Index size = internal::random<Index>(min_size, max_size); |
| |
| MatrixType m1(size, size), m2(size, size), m3(size, size); |
| m1 = MatrixType::Random(size, size); |
| |
| if (internal::is_same<RealScalar, float>::value) { |
| // let's build a matrix more stable to inverse |
| MatrixType a = MatrixType::Random(size, size * 2); |
| m1 += a * a.adjoint(); |
| } |
| |
| FullPivHouseholderQR<MatrixType> qr(m1); |
| VERIFY(qr.isInjective()); |
| VERIFY(qr.isInvertible()); |
| VERIFY(qr.isSurjective()); |
| |
| check_solverbase<MatrixType, MatrixType>(m1, qr, size, size, size); |
| |
| // now construct a matrix with prescribed determinant |
| m1.setZero(); |
| for (int i = 0; i < size; i++) m1(i, i) = internal::random<Scalar>(); |
| Scalar det = m1.diagonal().prod(); |
| RealScalar absdet = abs(det); |
| m3 = qr.matrixQ(); // get a unitary |
| m1 = m3 * m1 * m3.adjoint(); |
| qr.compute(m1); |
| VERIFY_IS_APPROX(det, qr.determinant()); |
| VERIFY_IS_APPROX(absdet, qr.absDeterminant()); |
| VERIFY_IS_APPROX(log(absdet), qr.logAbsDeterminant()); |
| } |
| |
| template <typename MatrixType> |
| void qr_verify_assert() { |
| MatrixType tmp; |
| |
| FullPivHouseholderQR<MatrixType> qr; |
| VERIFY_RAISES_ASSERT(qr.matrixQR()) |
| VERIFY_RAISES_ASSERT(qr.solve(tmp)) |
| VERIFY_RAISES_ASSERT(qr.transpose().solve(tmp)) |
| VERIFY_RAISES_ASSERT(qr.adjoint().solve(tmp)) |
| VERIFY_RAISES_ASSERT(qr.matrixQ()) |
| VERIFY_RAISES_ASSERT(qr.dimensionOfKernel()) |
| VERIFY_RAISES_ASSERT(qr.isInjective()) |
| VERIFY_RAISES_ASSERT(qr.isSurjective()) |
| VERIFY_RAISES_ASSERT(qr.isInvertible()) |
| VERIFY_RAISES_ASSERT(qr.inverse()) |
| VERIFY_RAISES_ASSERT(qr.determinant()) |
| VERIFY_RAISES_ASSERT(qr.absDeterminant()) |
| VERIFY_RAISES_ASSERT(qr.logAbsDeterminant()) |
| } |
| |
| EIGEN_DECLARE_TEST(qr_fullpivoting) { |
| for (int i = 0; i < 1; i++) { |
| CALL_SUBTEST_5(qr<Matrix3f>()); |
| CALL_SUBTEST_6(qr<Matrix3d>()); |
| CALL_SUBTEST_8(qr<Matrix2f>()); |
| CALL_SUBTEST_1(qr<MatrixXf>()); |
| CALL_SUBTEST_2(qr<MatrixXd>()); |
| CALL_SUBTEST_3(qr<MatrixXcd>()); |
| } |
| |
| for (int i = 0; i < g_repeat; i++) { |
| CALL_SUBTEST_1(qr_invertible<MatrixXf>()); |
| CALL_SUBTEST_2(qr_invertible<MatrixXd>()); |
| CALL_SUBTEST_4(qr_invertible<MatrixXcf>()); |
| CALL_SUBTEST_3(qr_invertible<MatrixXcd>()); |
| } |
| |
| CALL_SUBTEST_5(qr_verify_assert<Matrix3f>()); |
| CALL_SUBTEST_6(qr_verify_assert<Matrix3d>()); |
| CALL_SUBTEST_1(qr_verify_assert<MatrixXf>()); |
| CALL_SUBTEST_2(qr_verify_assert<MatrixXd>()); |
| CALL_SUBTEST_4(qr_verify_assert<MatrixXcf>()); |
| CALL_SUBTEST_3(qr_verify_assert<MatrixXcd>()); |
| |
| // Test problem size constructors |
| CALL_SUBTEST_7(FullPivHouseholderQR<MatrixXf>(10, 20)); |
| CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float, 10, 20> >(10, 20))); |
| CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float, 10, 20> >(Matrix<float, 10, 20>::Random()))); |
| CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float, 20, 10> >(20, 10))); |
| CALL_SUBTEST_7((FullPivHouseholderQR<Matrix<float, 20, 10> >(Matrix<float, 20, 10>::Random()))); |
| } |