QRFactorization.cpp

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#include <Eigen/Core>
#include <algorithm> // std::sort
#include <cmath>
#include <cstddef>
#include <iostream>
#include <memory>
#include <utility>
#include <vector>
 
#include "acceleration/Acceleration.hpp"
#include "acceleration/impl/QRFactorization.hpp"
#include "com/Communication.hpp"
#include "com/SharedPointer.hpp"
#include "logging/LogMacros.hpp"
#include "precice/impl/Types.hpp"
#include "utils/IntraComm.hpp"
#include "utils/assertion.hpp"
 
namespace precice::acceleration::impl {
 
QRFactorization::QRFactorization(
    Eigen::MatrixXd Q,
    Eigen::MatrixXd R,
    int             rows,
    int             cols,
    int             filter,
    double          omega,
    double          theta,
    double          sigma)
 
    : _Q(std::move(Q)),
      _R(std::move(R)),
      _rows(rows),
      _cols(cols),
      _filter(filter),
      _omega(omega),
      _theta(theta),
      _sigma(sigma),
      _infostream(),
      _fstream_set(false),
      _globalRows(rows)
{
  PRECICE_ASSERT(_R.rows() == _cols, _R.rows(), _cols);
  PRECICE_ASSERT(_R.cols() == _cols, _R.cols(), _cols);
  PRECICE_ASSERT(_Q.cols() == _cols, _Q.cols(), _cols);
  PRECICE_ASSERT(_Q.rows() == _rows, _Q.rows(), _rows);
}

QRFactorization::QRFactorization(
    Eigen::MatrixXd A,
    int             filter,
    double          omega,
    double          theta,
    double          sigma)
    : _Q(),
      _R(),
      _rows(A.rows()),
      _cols(0),
      _filter(filter),
      _omega(omega),
      _theta(theta),
      _sigma(sigma),
      _infostream(),
      _fstream_set(false),
      _globalRows(A.rows())
{
  int m = A.cols();
  for (int k = 0; k < m; k++) {
    Eigen::VectorXd v = A.col(k);
    insertColumn(k, v);
  }
  // PRECICE_ASSERT(_R.rows() == _cols, _R.rows(), _cols);
  PRECICE_ASSERT(_R.cols() == _cols, _R.cols(), _cols);
  PRECICE_ASSERT(_Q.cols() == _cols, _Q.cols(), _cols);
  PRECICE_ASSERT(_Q.rows() == _rows, _Q.rows(), _rows);
  PRECICE_ASSERT(_cols == m, _cols, m);
}

QRFactorization::QRFactorization(
    int    filter,
    double omega,
    double theta,
    double sigma)
    : _Q(),
      _R(),
      _rows(0),
      _cols(0),
      _filter(filter),
      _omega(omega),
      _theta(theta),
      _sigma(sigma),
      _infostream(),
      _fstream_set(false),
      _globalRows(0)
{
}
 
void QRFactorization::requireQR3Fallback()
{
  PRECICE_ASSERT(_filter == Acceleration::QR3FILTER);
  _computeQR2Filter = true;
}
 
void QRFactorization::applyFilter(double singularityLimit, std::vector<int> &delIndices, Eigen::MatrixXd &V)
{
  PRECICE_TRACE();
  delIndices.resize(0);
  if (_filter == Acceleration::QR1FILTER || _filter == Acceleration::QR1FILTER_ABS) {
    bool             linearDependence = true;
    std::vector<int> delFlag(_cols, 0);
    int              delCols = 0;
    while (linearDependence) {
      linearDependence = false;
      int index        = 0; // actual index of checked column, \in [0, _cols] and _cols is decreasing
      if (_cols > 1) {
        for (size_t i = 0; i < delFlag.size(); i++) {
          // index is not incremented, if columns has been deleted in previous rounds
          if (delFlag[i] > 0)
            continue;
 
          // QR1-filter
          if (index >= cols())
            break;
          PRECICE_ASSERT(index < _cols, index, _cols);
          double factor = (_filter == Acceleration::QR1FILTER_ABS) ? 1.0 : _R.norm();
          if (std::fabs(_R(index, index)) < singularityLimit * factor) {
 
            linearDependence = true;
            deleteColumn(index);
            delFlag[i]++;
            delIndices.push_back(i);
            delCols++;
            // break;
            index--; // check same column index, as cols are shifted left
          }
          PRECICE_ASSERT(delCols + _cols == (int) delFlag.size(), (delCols + _cols), delFlag.size());
          index++;
        }
      }
    }
  } else if (_filter == Acceleration::QR2FILTER) {
    resetFilter(singularityLimit, delIndices, V);
 
  } else if (_filter == Acceleration::QR3FILTER) {
    int index = cols() - 1;
    // Iterate from the last column to the 2nd column from the left
    for (size_t i = index; i > 0; i--) {
      Eigen::VectorXd v    = V.col(i);
      double          rho0 = utils::IntraComm::l2norm(v);
      if (std::fabs(_R(i, i)) < rho0 * singularityLimit) {
        _computeQR2Filter = true;
        break;
      }
    }
    if (_computeQR2Filter) {
      PRECICE_DEBUG("Pre-scaling weights were reset. Reverting to QR2 and rebuilding QR.");
      resetFilter(singularityLimit, delIndices, V);
    }
    _computeQR2Filter = false;
    PRECICE_DEBUG("Deleting {} columns in QR3 Filter", delIndices.size());
  }
  std::sort(delIndices.begin(), delIndices.end());
}

void QRFactorization::resetFilter(double singularityLimit, std::vector<int> &delIndices, Eigen::MatrixXd &V)
{
  _Q.resize(0, 0);
  _R.resize(0, 0);
  _cols = 0;
  _rows = V.rows();
  // starting with the most recent input/output information, i.e., the latest column
  // which is at position 0 in _matrixV (latest information is never filtered out!)
  for (int k = 0; k < V.cols(); k++) {
    Eigen::VectorXd v = V.col(k);
    // this is the same as pushBack(v) as _cols grows within the insertion process
    bool inserted = insertColumn(_cols, v, singularityLimit);
    if (!inserted) {
      delIndices.push_back(k);
    }
  }
  _resetFilterCounter++;
}

void QRFactorization::deleteColumn(int k)
{
 
  PRECICE_TRACE();
 
  PRECICE_ASSERT(k >= 0, k);
  PRECICE_ASSERT(k < _cols, k, _cols);
 
  // maintain decomposition and orthogonalization by application of givens rotations
 
  for (int l = k; l < _cols - 1; l++) {
    QRFactorization::givensRot grot;
    computeReflector(grot, _R(l, l + 1), _R(l + 1, l + 1));
    Eigen::VectorXd Rr1 = _R.row(l);
    Eigen::VectorXd Rr2 = _R.row(l + 1);
    applyReflector(grot, l + 2, _cols, Rr1, Rr2);
    _R.row(l)           = Rr1;
    _R.row(l + 1)       = Rr2;
    Eigen::VectorXd Qc1 = _Q.col(l);
    Eigen::VectorXd Qc2 = _Q.col(l + 1);
    applyReflector(grot, 0, _rows, Qc1, Qc2);
    _Q.col(l)     = Qc1;
    _Q.col(l + 1) = Qc2;
  }
  // copy values and resize R and Q
  for (int j = k; j < _cols - 1; j++) {
    for (int i = 0; i <= j; i++) {
      _R(i, j) = _R(i, j + 1);
    }
  }
  _R.conservativeResize(_cols - 1, _cols - 1);
  //_Q.conservativeResize(Eigen::NoChange_t, _cols-1);
  _Q.conservativeResize(_rows, _cols - 1);
  _cols--;
 
  PRECICE_ASSERT(_Q.cols() == _cols, _Q.cols(), _cols);
  PRECICE_ASSERT(_Q.rows() == _rows, _Q.rows(), _rows);
  PRECICE_ASSERT(_R.cols() == _cols, _R.cols(), _cols);
  // PRECICE_ASSERT(_R.rows() == _cols, _Q.rows(), _cols);
}
 
// ATTENTION: This method works on the memory of vector v, thus changes the vector v.
bool QRFactorization::insertColumn(int k, const Eigen::VectorXd &vec, double singularityLimit)
{
  PRECICE_TRACE(k);
 
  Eigen::VectorXd v(vec);
 
  if (_cols == 0)
    _rows = v.size();
 
  bool applyFilter = (singularityLimit > 0.0);
 
  PRECICE_ASSERT(k >= 0, k);
  PRECICE_ASSERT(k <= _cols, k, _cols);
  PRECICE_ASSERT(v.size() == _rows, v.size(), _rows);
 
  _cols++;
 
  // orthogonalize v to columns of Q
  Eigen::VectorXd u(_cols);
  double          rho_orth = 0., rho0 = 0.;
  if (applyFilter)
    rho0 = utils::IntraComm::l2norm(v);
 
  int err = orthogonalize(v, u, rho_orth, _cols - 1);
 
  // on of the following is true
  // - either ||v_orth|| / ||v|| <= 0.7 was true and the re-orthogonalization process failed 4 times
  // - or the system is quadratic, thus no further column can be orthogonalized (||v_orth|| = rho_orth = 0)
  // - or ||v_orth|| = rho_orth extremely small (almost zero), thus the column v is not very orthogonal to Q, discard
  if (rho_orth <= std::numeric_limits<double>::min() || err < 0) {
    PRECICE_DEBUG("The ratio ||v_orth|| / ||v|| is extremely small and either the orthogonalization process of column v failed or the system is quadratic.");
 
    // necessary for applyFilter with the QR-2 filter. In this case, the new column is not inserted, but discarded.
    _cols--;
    _computeQR2Filter = true;
    return false;
  }
 
  // QR2-filter based on the new information added to the orthogonal system.
  // if the new column incorporates less new information to the system than a
  // prescribed threshold, the column is discarded
  // rho_orth: the norm of the orthogonalized (but not normalized) column
  // rho0:     the norm of the initial column that is to be inserted
  if (applyFilter && (rho0 * singularityLimit > rho_orth)) {
    PRECICE_DEBUG("discarding column as it is filtered out by the QR2-filter: rho0*eps > rho_orth: {} > {}", rho0 * singularityLimit, rho_orth);
    _cols--;
    _computeQR2Filter = true;
    return false;
  }
 
  // resize R(1:m, 1:m) -> R(1:m+1, 1:m+1)
  _R.conservativeResize(_cols, _cols);
  _R.col(_cols - 1) = Eigen::VectorXd::Zero(_cols);
  _R.row(_cols - 1) = Eigen::VectorXd::Zero(_cols);
 
  for (int j = _cols - 2; j >= k; j--) {
    for (int i = 0; i <= j; i++) {
      _R(i, j + 1) = _R(i, j);
    }
  }
 
  for (int j = k + 1; j < _cols; j++) {
    _R(j, j) = 0.;
  }
 
  PRECICE_ASSERT(_R.cols() == _cols, _R.cols(), _cols);
  // PRECICE_ASSERT(_R.rows() == _cols, _R.rows(), _cols);
 
  // resize Q(1:n, 1:m) -> Q(1:n, 1:m+1)
  _Q.conservativeResize(_rows, _cols);
  _Q.col(_cols - 1) = v;
 
  PRECICE_ASSERT(_Q.cols() == _cols, _Q.cols(), _cols);
  PRECICE_ASSERT(_Q.rows() == _rows, _Q.rows(), _rows);
 
  // maintain decomposition and orthogonalization by application of givens rotations
  for (int l = _cols - 2; l >= k; l--) {
    QRFactorization::givensRot grot;
    computeReflector(grot, u(l), u(l + 1));
    Eigen::VectorXd Rr1 = _R.row(l);
    Eigen::VectorXd Rr2 = _R.row(l + 1);
    applyReflector(grot, l + 1, _cols, Rr1, Rr2);
    _R.row(l)           = Rr1;
    _R.row(l + 1)       = Rr2;
    Eigen::VectorXd Qc1 = _Q.col(l);
    Eigen::VectorXd Qc2 = _Q.col(l + 1);
    applyReflector(grot, 0, _rows, Qc1, Qc2);
    _Q.col(l)     = Qc1;
    _Q.col(l + 1) = Qc2;
  }
  for (int i = 0; i <= k; i++) {
    _R(i, k) = u(i);
  }
 
  return true;
}

int QRFactorization::orthogonalize(
    Eigen::VectorXd &v,
    Eigen::VectorXd &r,
    double          &rho,
    int              colNum)
{
  PRECICE_TRACE();
 
  if (!utils::IntraComm::isParallel()) {
    PRECICE_ASSERT(_globalRows == _rows, _globalRows, _rows);
  }
 
  bool            null        = false;
  bool            termination = false;
  double          rho0 = 0., rho1 = 0.;
  Eigen::VectorXd u = Eigen::VectorXd::Zero(_rows);
  Eigen::VectorXd s = Eigen::VectorXd::Zero(colNum);
  r                 = Eigen::VectorXd::Zero(_cols);
 
  rho   = utils::IntraComm::l2norm(v); // distributed l2norm
  rho0  = rho;
  int k = 0;
  while (!termination) {
 
    // take a gram-schmidt iteration
    u = Eigen::VectorXd::Zero(_rows);
    for (int j = 0; j < colNum; j++) {
 
      // dot-product <_Q(:,j), v >
      Eigen::VectorXd Qc = _Q.col(j);
 
      // dot product <_Q(:,j), v> =: r_ij
      double r_ij = utils::IntraComm::dot(Qc, v);
      // save r_ij in s(j) = column of R
      s(j) = r_ij;
      // u is the sum of projections r_ij * _Q(:,j) =  _Q(:,j) * <_Q(:,j), v>
      u += _Q.col(j) * r_ij;
    }
    // add the furier coefficients over all orthogonalize iterations
    for (int j = 0; j < colNum; j++) {
      r(j) = r(j) + s(j);
    }
    // subtract projections from v, v is now orthogonal to columns of _Q
    for (int i = 0; i < _rows; i++) {
      v(i) = v(i) - u(i);
    }
    // rho1 = norm of orthogonalized new column v_tilde (though not normalized)
    rho1 = utils::IntraComm::l2norm(v); // distributed l2norm
 
    // t = norm of r_(:,j) with j = colNum-1
    double norm_coefficients = utils::IntraComm::l2norm(s); // distributed l2norm
    k++;
 
    // treat the special case m=n
    // Attention (intra-participant communication): Here, we need to compare the global _rows with colNum and NOT the local
    // rows on the processor.
    if (_globalRows == colNum) {
      PRECICE_WARN("The least-squares system matrix is quadratic, i.e., the new column cannot be orthogonalized (and thus inserted) to the LS-system.\nOld columns need to be removed.");
      v   = Eigen::VectorXd::Zero(_rows);
      rho = 0.;
      return k;
    }
 
    // take correct action if v_orth is null
    if (rho1 <= std::numeric_limits<double>::min()) {
      PRECICE_DEBUG("The norm of v_orthogonal is almost zero, i.e., failed to orthogonalize column v; discard.");
      null        = true;
      rho1        = 1;
      termination = true;
    }

    if (rho1 * _theta <= rho0 + _omega * norm_coefficients) {
      // exit to fail if too many iterations
      if (k >= 4) {
        PRECICE_WARN("Matrix Q is not sufficiently orthogonal. Failed to orthogonalize new column after 4 iterations. New column will be discarded. The least-squares system is very bad conditioned and the quasi-Newton will most probably fail to converge.");
        return -1;
      }
      rho0 = rho1;
 
      // termination, i.e., (rho0 + _omega * t < _theta *rho1)
    } else {
      termination = true;
    }
  }
 
  // normalize v
  v /= rho1;
  rho       = null ? 0 : rho1;
  r(colNum) = rho;
  return k;
}

int QRFactorization::orthogonalize_stable(
    Eigen::VectorXd &v,
    Eigen::VectorXd &r,
    double          &rho,
    int              colNum)
{
  PRECICE_TRACE();
 
  // serial case
  if (!utils::IntraComm::isParallel()) {
    PRECICE_ASSERT(_globalRows == _rows, _globalRows, _rows);
  }
 
  bool            restart     = false;
  bool            null        = false;
  bool            termination = false;
  double          rho0 = 0., rho1 = 0.;
  double          t = 0;
  Eigen::VectorXd u = Eigen::VectorXd::Zero(_rows);
  Eigen::VectorXd s = Eigen::VectorXd::Zero(colNum);
  r                 = Eigen::VectorXd::Zero(_cols);
 
  rho   = utils::IntraComm::l2norm(v); // distributed l2norm
  rho0  = rho;
  int k = 0;
  while (!termination) {
    // take a gram-schmidt iteration, ignoring r on later steps if previous v was null
    u = Eigen::VectorXd::Zero(_rows);
    for (int j = 0; j < colNum; j++) {
 
      /*
       * dot-product <_Q(:,j), v >
       */
      Eigen::VectorXd Qc = _Q.col(j);
 
      // dot product <_Q(:,j), v> =: r_ij
      double ss = utils::IntraComm::dot(Qc, v);
      t         = ss;
      // save r_ij in s(j) = column of R
      s(j) = t;
      // u is the sum of projections r_ij * _Q(i,:) =  _Q(i,:) * <_Q(:,j), v>
      for (int i = 0; i < _rows; i++) {
        u(i) = u(i) + _Q(i, j) * t;
      }
    }
    if (!null) {
      // add over all runs: r_ij = r_ij_prev + r_ij
      for (int j = 0; j < colNum; j++) {
        r(j) = r(j) + s(j);
      }
    }
    // subtract projections from v, v is now orthogonal to columns of _Q
    for (int i = 0; i < _rows; i++) {
      v(i) = v(i) - u(i);
    }
    // rho1 = norm of orthogonalized new column v_tilde (though not normalized)
    rho1 = utils::IntraComm::l2norm(v); // distributed l2norm
 
    // t = norm of r_(:,j) with j = colNum-1
    t = utils::IntraComm::l2norm(s); // distributed l2norm
    k++;
 
    // treat the special case m=n
    // Attention (intra-participant communication): Here, we need to compare the global _rows with colNum and NOT the local
    // rows on the processor.
    if (_globalRows == colNum) {
      PRECICE_WARN("The least-squares system matrix is quadratic, i.e., the new column cannot be orthogonalized (and thus inserted) to the LS-system.\nOld columns need to be removed.");
      v   = Eigen::VectorXd::Zero(_rows);
      rho = 0.;
      return k;
    }

    if (rho1 * _theta <= rho0 + _omega * t) {
      // exit to fail if too many iterations
      if (k >= 4) {
        std::cout
            << "\ntoo many iterations in orthogonalize, termination failed\n";
        PRECICE_WARN("Matrix Q is not sufficiently orthogonal. Failed to orthogonalize new column after 4 iterations. New column will be discarded. The least-squares system is very bad conditioned and the quasi-Newton will most probably fail to converge.");
        return -1;
      }
 
      // if ||v_orth|| / ||v|| is extremely small (numeric limit)
      // discard information from column, use any unit vector orthogonal to Q
      if (!restart && rho1 <= rho * _sigma) {
        PRECICE_WARN("The new column is in the range of Q, thus not possible to orthogonalize. Try to insert a unit vector that is orthogonal to the columns space of Q.");
        // PRECICE_DEBUG("[QR-dec] - reorthogonalization");
        if (_fstream_set)
          (*_infostream) << "[QR-dec] - reorthogonalization\n";
 
        restart = true;

        u = Eigen::VectorXd::Zero(_rows);
        for (int j = 0; j < colNum; j++) {
          for (int i = 0; i < _rows; i++) {
            u(i) = u(i) + _Q(i, j) * _Q(i, j);
          }
        }
        t = 2;
 
        // ATTENTION: maybe in the following is something wrong @todo: fix this comment
 
        for (int i = 0; i < _rows; i++) {
          if (u(i) < t) {
            k = i;
            t = u(k);
          }
        }
 
        int    global_k  = k;
        int    local_k   = 0;
        double local_uk  = 0.;
        double global_uk = 0.;
        int    rank      = 0;
 
        if (utils::IntraComm::isSecondary()) {
          utils::IntraComm::getCommunication()->send(k, 0);
          utils::IntraComm::getCommunication()->send(u(k), 0);
        }
 
        if (utils::IntraComm::isPrimary()) {
          global_uk = u(k);
          for (Rank secondaryRank : utils::IntraComm::allSecondaryRanks()) {
            utils::IntraComm::getCommunication()->receive(local_k, secondaryRank);
            utils::IntraComm::getCommunication()->receive(local_uk, secondaryRank);
            if (local_uk < global_uk) {
              rank      = secondaryRank;
              global_uk = local_uk;
              global_k  = local_k;
            }
          }
          if (_fstream_set)
            (*_infostream) << "           global u(k):" << global_uk << ",  global k: " << global_k << ",  rank: " << rank << '\n';
        }
 
        // take correct action if v is null
        if (rho1 == 0) {
          null = true;
          rho1 = 1;
        }
        // reinitialize v and k
        v = Eigen::VectorXd::Zero(_rows);
 
        // insert rho1 at position k with smallest u(i) = Q(i,:) * Q(i,:)
        if (!utils::IntraComm::isParallel()) {
          v(k) = rho1;
        } else {
          if (utils::IntraComm::getRank() == rank)
            v(global_k) = rho1;
        }
        k = 0;
      }
      rho0 = rho1;
 
      // termination, i.e., (rho0 + _omega * t < _theta *rho1)
    } else {
      termination = true;
    }
  }
 
  // normalize v
  v /= rho1;
  rho       = null ? 0 : rho1;
  r(colNum) = rho;
  return k;
}

void QRFactorization::computeReflector(
    QRFactorization::givensRot &grot,
    double                     &x,
    double                     &y)
{
  double u = x;
  double v = y;
  if (v == 0) {
    grot.sigma = 0;
    grot.gamma = 1;
  } else {
    double mu = std::max(std::fabs(u), std::fabs(v));
    double t  = mu * std::sqrt(std::pow(u / mu, 2) + std::pow(v / mu, 2));
    t *= (u < 0) ? -1 : 1;
    grot.gamma = u / t;
    grot.sigma = v / t;
    x          = t;
    y          = 0;
  }
}

void QRFactorization::applyReflector(
    const QRFactorization::givensRot &grot,
    int                               k,
    int                               l,
    Eigen::VectorXd                  &p,
    Eigen::VectorXd                  &q)
{
  double nu = grot.sigma / (1. + grot.gamma);
  for (int j = k; j < l; j++) {
    double u = p(j);
    double v = q(j);
    double t = u * grot.gamma + v * grot.sigma;
    p(j)     = t;
    q(j)     = (t + u) * nu - v;
  }
}
 
void QRFactorization::setGlobalRows(int gr)
{
  _globalRows = gr;
}
 
Eigen::MatrixXd &QRFactorization::matrixQ()
{
  return _Q;
}
 
Eigen::MatrixXd &QRFactorization::matrixR()
{
  return _R;
}
 
int QRFactorization::cols() const
{
  return _cols;
}
 
int QRFactorization::rows() const
{
  return _rows;
}
 
size_t QRFactorization::getResetFilterCounter() const
{
  return _resetFilterCounter;
}
 
void QRFactorization::reset()
{
  _Q.resize(0, 0);
  _R.resize(0, 0);
  _cols       = 0;
  _rows       = 0;
  _globalRows = 0;
}
 
void QRFactorization::reset(
    Eigen::MatrixXd const &Q,
    Eigen::MatrixXd const &R,
    int                    rows,
    int                    cols,
    double                 omega,
    double                 theta,
    double                 sigma)
{
  _Q          = Q;
  _R          = R;
  _rows       = rows;
  _cols       = cols;
  _omega      = omega;
  _theta      = theta;
  _sigma      = sigma;
  _globalRows = _rows;
  PRECICE_ASSERT(_R.rows() == _cols, _R.rows(), _cols);
  PRECICE_ASSERT(_R.cols() == _cols, _R.cols(), _cols);
  PRECICE_ASSERT(_Q.cols() == _cols, _Q.cols(), _cols);
  PRECICE_ASSERT(_Q.rows() == _rows, _Q.rows(), _rows);
}
 
void QRFactorization::reset(
    Eigen::MatrixXd const &A,
    int                    globalRows,
    double                 omega,
    double                 theta,
    double                 sigma)
{
  PRECICE_TRACE();
  _Q.resize(0, 0);
  _R.resize(0, 0);
  _cols       = 0;
  _rows       = A.rows();
  _omega      = omega;
  _theta      = theta;
  _sigma      = sigma;
  _globalRows = globalRows;
 
  int m   = A.cols();
  int col = 0, k = 0;
  for (; col < m; k++, col++) {
    Eigen::VectorXd v        = A.col(col);
    bool            inserted = insertColumn(k, v);
    if (not inserted) {
      k--;
      PRECICE_DEBUG("column {} has not been inserted in the QR-factorization, failed to orthogonalize.", col);
    }
  }
  PRECICE_ASSERT(_R.rows() == _cols, _R.rows(), _cols);
  PRECICE_ASSERT(_R.cols() == _cols, _R.cols(), _cols);
  PRECICE_ASSERT(_Q.cols() == _cols, _Q.cols(), _cols);
  PRECICE_ASSERT(_Q.rows() == _rows, _Q.rows(), _rows);
  PRECICE_ASSERT(_cols == m, _cols, m);
}
 
void QRFactorization::pushFront(const Eigen::VectorXd &v)
{
  insertColumn(0, v);
}
 
void QRFactorization::pushBack(const Eigen::VectorXd &v)
{
  insertColumn(_cols, v);
}
 
void QRFactorization::popFront()
{
  deleteColumn(0);
}
 
void QRFactorization::popBack()
{
  deleteColumn(_cols - 1);
}
 
void QRFactorization::setfstream(std::fstream *stream)
{
  _infostream  = stream;
  _fstream_set = true;
}
 
void QRFactorization::setFilter(int filter)
{
  _filter = filter;
}
 
} // namespace precice::acceleration::impl