FastDeploy/third_party/eigen/test/householder.cpp

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 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 <Eigen/QR>
#include "main.h"

template <typename MatrixType>
void householder(const MatrixType& m) {
  static bool even = true;
  even = !even;
  /* this test covers the following files:
     Householder.h
  */
  Index rows = m.rows();
  Index cols = m.cols();

  typedef typename MatrixType::Scalar Scalar;
  typedef typename NumTraits<Scalar>::Real RealScalar;
  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
  typedef Matrix<
      Scalar, internal::decrement_size<MatrixType::RowsAtCompileTime>::ret, 1>
      EssentialVectorType;
  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime,
                 MatrixType::RowsAtCompileTime>
      SquareMatrixType;
  typedef Matrix<Scalar, Dynamic, MatrixType::ColsAtCompileTime>
      HBlockMatrixType;
  typedef Matrix<Scalar, Dynamic, 1> HCoeffsVectorType;

  typedef Matrix<Scalar, MatrixType::ColsAtCompileTime,
                 MatrixType::RowsAtCompileTime>
      TMatrixType;

  Matrix<Scalar, EIGEN_SIZE_MAX(MatrixType::RowsAtCompileTime,
                                MatrixType::ColsAtCompileTime),
         1>
      _tmp((std::max)(rows, cols));
  Scalar* tmp = &_tmp.coeffRef(0, 0);

  Scalar beta;
  RealScalar alpha;
  EssentialVectorType essential;

  VectorType v1 = VectorType::Random(rows), v2;
  v2 = v1;
  v1.makeHouseholder(essential, beta, alpha);
  v1.applyHouseholderOnTheLeft(essential, beta, tmp);
  VERIFY_IS_APPROX(v1.norm(), v2.norm());
  if (rows >= 2)
    VERIFY_IS_MUCH_SMALLER_THAN(v1.tail(rows - 1).norm(), v1.norm());
  v1 = VectorType::Random(rows);
  v2 = v1;
  v1.applyHouseholderOnTheLeft(essential, beta, tmp);
  VERIFY_IS_APPROX(v1.norm(), v2.norm());

  // reconstruct householder matrix:
  SquareMatrixType id, H1, H2;
  id.setIdentity(rows, rows);
  H1 = H2 = id;
  VectorType vv(rows);
  vv << Scalar(1), essential;
  H1.applyHouseholderOnTheLeft(essential, beta, tmp);
  H2.applyHouseholderOnTheRight(essential, beta, tmp);
  VERIFY_IS_APPROX(H1, H2);
  VERIFY_IS_APPROX(H1, id - beta * vv * vv.adjoint());

  MatrixType m1(rows, cols), m2(rows, cols);

  v1 = VectorType::Random(rows);
  if (even) v1.tail(rows - 1).setZero();
  m1.colwise() = v1;
  m2 = m1;
  m1.col(0).makeHouseholder(essential, beta, alpha);
  m1.applyHouseholderOnTheLeft(essential, beta, tmp);
  VERIFY_IS_APPROX(m1.norm(), m2.norm());
  if (rows >= 2)
    VERIFY_IS_MUCH_SMALLER_THAN(m1.block(1, 0, rows - 1, cols).norm(),
                                m1.norm());
  VERIFY_IS_MUCH_SMALLER_THAN(numext::imag(m1(0, 0)), numext::real(m1(0, 0)));
  VERIFY_IS_APPROX(numext::real(m1(0, 0)), alpha);

  v1 = VectorType::Random(rows);
  if (even) v1.tail(rows - 1).setZero();
  SquareMatrixType m3(rows, rows), m4(rows, rows);
  m3.rowwise() = v1.transpose();
  m4 = m3;
  m3.row(0).makeHouseholder(essential, beta, alpha);
  m3.applyHouseholderOnTheRight(essential.conjugate(), beta, tmp);
  VERIFY_IS_APPROX(m3.norm(), m4.norm());
  if (rows >= 2)
    VERIFY_IS_MUCH_SMALLER_THAN(m3.block(0, 1, rows, rows - 1).norm(),
                                m3.norm());
  VERIFY_IS_MUCH_SMALLER_THAN(numext::imag(m3(0, 0)), numext::real(m3(0, 0)));
  VERIFY_IS_APPROX(numext::real(m3(0, 0)), alpha);

  // test householder sequence on the left with a shift

  Index shift = internal::random<Index>(0, std::max<Index>(rows - 2, 0));
  Index brows = rows - shift;
  m1.setRandom(rows, cols);
  HBlockMatrixType hbm = m1.block(shift, 0, brows, cols);
  HouseholderQR<HBlockMatrixType> qr(hbm);
  m2 = m1;
  m2.block(shift, 0, brows, cols) = qr.matrixQR();
  HCoeffsVectorType hc = qr.hCoeffs().conjugate();
  HouseholderSequence<MatrixType, HCoeffsVectorType> hseq(m2, hc);
  hseq.setLength(hc.size()).setShift(shift);
  VERIFY(hseq.length() == hc.size());
  VERIFY(hseq.shift() == shift);

  MatrixType m5 = m2;
  m5.block(shift, 0, brows, cols)
      .template triangularView<StrictlyLower>()
      .setZero();
  VERIFY_IS_APPROX(hseq * m5, m1);  // test applying hseq directly
  m3 = hseq;
  VERIFY_IS_APPROX(
      m3 * m5, m1);  // test evaluating hseq to a dense matrix, then applying

  SquareMatrixType hseq_mat = hseq;
  SquareMatrixType hseq_mat_conj = hseq.conjugate();
  SquareMatrixType hseq_mat_adj = hseq.adjoint();
  SquareMatrixType hseq_mat_trans = hseq.transpose();
  SquareMatrixType m6 = SquareMatrixType::Random(rows, rows);
  VERIFY_IS_APPROX(hseq_mat.adjoint(), hseq_mat_adj);
  VERIFY_IS_APPROX(hseq_mat.conjugate(), hseq_mat_conj);
  VERIFY_IS_APPROX(hseq_mat.transpose(), hseq_mat_trans);
  VERIFY_IS_APPROX(hseq * m6, hseq_mat * m6);
  VERIFY_IS_APPROX(hseq.adjoint() * m6, hseq_mat_adj * m6);
  VERIFY_IS_APPROX(hseq.conjugate() * m6, hseq_mat_conj * m6);
  VERIFY_IS_APPROX(hseq.transpose() * m6, hseq_mat_trans * m6);
  VERIFY_IS_APPROX(m6 * hseq, m6 * hseq_mat);
  VERIFY_IS_APPROX(m6 * hseq.adjoint(), m6 * hseq_mat_adj);
  VERIFY_IS_APPROX(m6 * hseq.conjugate(), m6 * hseq_mat_conj);
  VERIFY_IS_APPROX(m6 * hseq.transpose(), m6 * hseq_mat_trans);

  // test householder sequence on the right with a shift

  TMatrixType tm2 = m2.transpose();
  HouseholderSequence<TMatrixType, HCoeffsVectorType, OnTheRight> rhseq(tm2,
                                                                        hc);
  rhseq.setLength(hc.size()).setShift(shift);
  VERIFY_IS_APPROX(rhseq * m5, m1);  // test applying rhseq directly
  m3 = rhseq;
  VERIFY_IS_APPROX(
      m3 * m5, m1);  // test evaluating rhseq to a dense matrix, then applying
}

EIGEN_DECLARE_TEST(householder) {
  for (int i = 0; i < g_repeat; i++) {
    CALL_SUBTEST_1(householder(Matrix<double, 2, 2>()));
    CALL_SUBTEST_2(householder(Matrix<float, 2, 3>()));
    CALL_SUBTEST_3(householder(Matrix<double, 3, 5>()));
    CALL_SUBTEST_4(householder(Matrix<float, 4, 4>()));
    CALL_SUBTEST_5(
        householder(MatrixXd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE),
                             internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
    CALL_SUBTEST_6(
        householder(MatrixXcf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE),
                              internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
    CALL_SUBTEST_7(
        householder(MatrixXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE),
                             internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
    CALL_SUBTEST_8(householder(Matrix<double, 1, 1>()));
  }
}