IQNIMVJAcceleration.cpp

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#ifndef PRECICE_NO_MPI
 
#include <Eigen/Core>
#include <algorithm>
#include <fstream>
#include <map>
#include <memory>
#include <string>
#include <utility>
 
#include "acceleration/IQNIMVJAcceleration.hpp"
#include "acceleration/impl/ParallelMatrixOperations.hpp"
#include "acceleration/impl/Preconditioner.hpp"
#include "acceleration/impl/QRFactorization.hpp"
#include "com/Communication.hpp"
#include "com/MPIPortsCommunication.hpp"
#include "cplscheme/CouplingData.hpp"
#include "cplscheme/SharedPointer.hpp"
#include "logging/LogMacros.hpp"
#include "precice/impl/Types.hpp"
#include "profiling/Event.hpp"
#include "utils/EigenHelperFunctions.hpp"
#include "utils/IntraComm.hpp"
#include "utils/assertion.hpp"
 
using precice::cplscheme::PtrCouplingData;
 
namespace precice::acceleration {
 
// ==================================================================================
IQNIMVJAcceleration::IQNIMVJAcceleration(
    double                         initialRelaxation,
    bool                           forceInitialRelaxation,
    int                            maxIterationsUsed,
    int                            pastTimeWindowsReused,
    int                            filter,
    double                         singularityLimit,
    std::vector<int>               dataIDs,
    const impl::PtrPreconditioner &preconditioner,
    bool                           alwaysBuildJacobian,
    int                            imvjRestartType,
    int                            chunkSize,
    int                            RSLSreusedTimeWindows,
    double                         RSSVDtruncationEps,
    bool                           reducedTimeGrid)
    : BaseQNAcceleration(initialRelaxation, forceInitialRelaxation, maxIterationsUsed, pastTimeWindowsReused,
                         filter, singularityLimit, std::move(dataIDs), preconditioner, reducedTimeGrid),
      //  _secondaryOldXTildes(),
      _invJacobian(),
      _oldInvJacobian(),
      _Wtil(),
      _WtilChunk(),
      _pseudoInverseChunk(),
      _matrixV_RSLS(),
      _matrixW_RSLS(),
      _matrixCols_RSLS(),
      _parMatrixOps(nullptr),
      _svdJ(RSSVDtruncationEps, preconditioner),
      _alwaysBuildJacobian(alwaysBuildJacobian),
      _imvjRestartType(imvjRestartType),
      _imvjRestart(imvjRestartType > 0),
      _chunkSize(chunkSize),
      _RSLSreusedTimeWindows(RSLSreusedTimeWindows),
      _nbRestarts(0),
      _avgRank(0)
{
}
 
// ==================================================================================
IQNIMVJAcceleration::~IQNIMVJAcceleration() = default;
 
// ==================================================================================
void IQNIMVJAcceleration::updateDifferenceMatrices(
    const DataMap &cplData)
{
 
  PRECICE_TRACE();
 
  // call the base method for common update of V, W matrices
  // important that base method is called before updating _Wtil
  BaseQNAcceleration::updateDifferenceMatrices(cplData);
 
  // update _Wtil if the efficient computation of the quasi-Newton update is used
  // or update current Wtil if the restart mode of imvj is used
  if (not _alwaysBuildJacobian || _imvjRestart) {
    if (_firstIteration && (_firstTimeWindow || _forceInitialRelaxation)) {
      // do nothing: constant relaxation
    } else {
      if (not _firstIteration) {
        // Update matrix _Wtil = (W - J_prev*V) with newest information
 
        Eigen::VectorXd v = _matrixV.col(0);
        Eigen::VectorXd w = _matrixW.col(0);
 
        // here, we check for _Wtil.cols() as the matrices V, W need to be updated before hand
        // and thus getLSSystemCols() does not yield the correct result.
        bool columnLimitReached = _Wtil.cols() == _maxIterationsUsed;
        bool overdetermined     = _Wtil.cols() <= getLSSystemRows();
 
        Eigen::VectorXd wtil = Eigen::VectorXd::Zero(_matrixW.rows());
 
        // add column: Wtil(:,0) = W(:,0) - sum_q [ Wtil^q * ( Z^q * V(:,0)) ]
        //                                         |--- J_prev ---|
        // iterate over all stored Wtil and Z matrices in current chunk
        if (_imvjRestart) {
          for (int i = 0; i < static_cast<int>(_WtilChunk.size()); i++) {
            int colsLSSystemBackThen = _pseudoInverseChunk[i].rows();
            PRECICE_ASSERT(colsLSSystemBackThen == _WtilChunk[i].cols(), colsLSSystemBackThen, _WtilChunk[i].cols());
            Eigen::VectorXd Zv = Eigen::VectorXd::Zero(colsLSSystemBackThen);
            // multiply: Zv := Z^q * V(:,0) of size (m x 1)
            _parMatrixOps->multiply(_pseudoInverseChunk[i], v, Zv, colsLSSystemBackThen, getLSSystemRows(), 1);
            // multiply: Wtil^q * Zv  dimensions: (n x m) * (m x 1), fully local
            wtil += _WtilChunk[i] * Zv;
          }
 
          // store columns if restart mode = RS-LS
          if (_imvjRestartType == RS_LS) {
            utils::appendFront(_matrixV_RSLS, v);
            utils::appendFront(_matrixW_RSLS, w);
            _matrixCols_RSLS.front()++;
          }
 
          // imvj without restart is used, but efficient update, i.e. no Jacobian assembly in each iteration
          // add column: Wtil(:,0) = W(:,0) - J_prev * V(:,0)
        } else {
          // compute J_prev * V(0) := wtil the new column in _Wtil of dimension: (n x n) * (n x 1) = (n x 1),
          //                                        parallel: (n_global x n_local) * (n_local x 1) = (n_local x 1)
          _parMatrixOps->multiply(_oldInvJacobian, v, wtil, _dimOffsets, getLSSystemRows(), getLSSystemRows(), 1, false, false);
        }
        wtil *= -1;
        wtil += w;
 
        if (not columnLimitReached && overdetermined) {
          utils::appendFront(_Wtil, wtil);
        } else {
          utils::shiftSetFirst(_Wtil, wtil);
        }
      }
    }
  }
}
 
// ==================================================================================
void IQNIMVJAcceleration::computeQNUpdate(Eigen::VectorXd &xUpdate)
{

  if (_alwaysBuildJacobian) {
    computeNewtonUpdate(xUpdate);
  } else {
    computeNewtonUpdateEfficient(xUpdate);
  }
}
 
// ==================================================================================
void IQNIMVJAcceleration::pseudoInverse(
    Eigen::MatrixXd &pseudoInverse)
{
  PRECICE_TRACE();
  auto Q = _qrV.matrixQ();
  auto R = _qrV.matrixR();
 
  PRECICE_ASSERT(pseudoInverse.rows() == _qrV.cols(), pseudoInverse.rows(), _qrV.cols());
  PRECICE_ASSERT(pseudoInverse.cols() == _qrV.rows(), pseudoInverse.cols(), _qrV.rows());
 
  Eigen::VectorXd yVec(pseudoInverse.rows());
 
  // assertions for the case of processors with no vertices
  if (!_hasNodesOnInterface) {
    PRECICE_ASSERT(_qrV.cols() == getLSSystemCols(), _qrV.cols(), getLSSystemCols());
    PRECICE_ASSERT(_qrV.rows() == 0, _qrV.rows());
    PRECICE_ASSERT(Q.size() == 0, Q.size());
  }
 
  // backsubstitution
  for (int i = 0; i < Q.rows(); i++) {
    Eigen::VectorXd Qrow = Q.row(i);
    yVec                 = R.triangularView<Eigen::Upper>().solve<Eigen::OnTheLeft>(Qrow);
    pseudoInverse.col(i) = yVec;
  } // ----------------
 
  // scale pseudo inverse back Z := Z' * P,
  // Z' is scaled pseudo inverse i.e, Z' = R^-1 * Q^T * P^-1
  _preconditioner->apply(pseudoInverse, true);
  //  e.stop(true);
}
 
// ==================================================================================
void IQNIMVJAcceleration::buildWtil()
{
  PRECICE_TRACE();
 
  PRECICE_ASSERT(_matrixV.rows() == _qrV.rows(), _matrixV.rows(), _qrV.rows());
  PRECICE_ASSERT(getLSSystemCols() == _qrV.cols(), getLSSystemCols(), _qrV.cols());
 
  _Wtil = Eigen::MatrixXd::Zero(_residuals.rows(), _qrV.cols());
 
  // imvj restart mode: re-compute Wtil: Wtil = W - sum_q [ Wtil^q * (Z^q*V) ]
  //                                                      |--- J_prev ---|
  // iterate over all stored Wtil and Z matrices in current chunk
  if (_imvjRestart) {
    for (int i = 0; i < static_cast<int>(_WtilChunk.size()); i++) {
      int colsLSSystemBackThen = _pseudoInverseChunk[i].rows();
      PRECICE_ASSERT(colsLSSystemBackThen == _WtilChunk[i].cols(), colsLSSystemBackThen, _WtilChunk[i].cols());
      Eigen::MatrixXd ZV = Eigen::MatrixXd::Zero(colsLSSystemBackThen, _qrV.cols());
      // multiply: ZV := Z^q * V of size (m x m) with m=#cols, stored on each proc.
      _parMatrixOps->multiply(_pseudoInverseChunk[i], _matrixV, ZV, colsLSSystemBackThen, getLSSystemRows(), _qrV.cols());
      // multiply: Wtil^q * ZV  dimensions: (n x m) * (m x m), fully local and embarrassingly parallel
      _Wtil += _WtilChunk[i] * ZV;
    }
 
    // imvj without restart is used, i.e., recompute Wtil: Wtil = W - J_prev * V
  } else {
    // multiply J_prev * V = W_til of dimension: (n x n) * (n x m) = (n x m),
    //                                    parallel:  (n_global x n_local) * (n_local x m) = (n_local x m)
    _parMatrixOps->multiply(_oldInvJacobian, _matrixV, _Wtil, _dimOffsets, getLSSystemRows(), getPrimaryLSSystemRows(), getLSSystemCols(), false, false);
  }
 
  // W_til = (W-J_inv_n*V) = (W-V_tilde)
  _Wtil *= -1.;
  _Wtil = _Wtil + _matrixW;
 
  _resetLS = false;
  //  e.stop(true);
}
 
// ==================================================================================
void IQNIMVJAcceleration::buildJacobian()
{
  PRECICE_TRACE();

  Eigen::MatrixXd Z(_qrV.cols(), _qrV.rows());
  pseudoInverse(Z);

  PRECICE_ASSERT(_matrixV.rows() == _qrV.rows(), _matrixV.rows(), _qrV.rows());
  PRECICE_ASSERT(getLSSystemCols() == _qrV.cols(), getLSSystemCols(), _qrV.cols());
  if (_resetLS) {
    buildWtil();
    PRECICE_WARN(" ATTENTION, in buildJacobian call for buildWtill() - this should not be the case except the coupling did only one iteration");
  }

  _parMatrixOps->multiply(_Wtil, Z, _invJacobian, _dimOffsets, getLSSystemRows(), getLSSystemCols(), getPrimaryLSSystemRows());
  // --------
 
  // update Jacobian
  _invJacobian = _invJacobian + _oldInvJacobian;
}
 
// ==================================================================================
void IQNIMVJAcceleration::computeNewtonUpdateEfficient(Eigen::VectorXd &xUpdate)
{
  PRECICE_TRACE();


  Eigen::MatrixXd Z(_qrV.cols(), _qrV.rows());
  pseudoInverse(Z);

  PRECICE_ASSERT(_matrixV.rows() == _qrV.rows(), _matrixV.rows(), _qrV.rows());
  PRECICE_ASSERT(getLSSystemCols() == _qrV.cols(), getLSSystemCols(), _qrV.cols());
 
  // rebuild matrix Wtil if V changes substantially.
  if (_resetLS) {
    buildWtil();
  }

  Eigen::VectorXd negativeResiduals = -_primaryResiduals;
  Eigen::VectorXd r_til             = Eigen::VectorXd::Zero(getLSSystemCols());
  _parMatrixOps->multiply(Z, negativeResiduals, r_til, getLSSystemCols(), getLSSystemRows(), 1); // --------

  Eigen::VectorXd xUptmp(_residuals.size());
  xUpdate = Eigen::VectorXd::Zero(_residuals.size());
  xUptmp  = _Wtil * r_til; // local product, result is naturally distributed.

  if (_imvjRestart) {
    for (int i = 0; i < static_cast<int>(_WtilChunk.size()); i++) {
      int colsLSSystemBackThen = _pseudoInverseChunk[i].rows();
      PRECICE_ASSERT(colsLSSystemBackThen == _WtilChunk[i].cols(), colsLSSystemBackThen, _WtilChunk[i].cols());
      r_til = Eigen::VectorXd::Zero(colsLSSystemBackThen);
      // multiply: r_til := Z^q * (-res) of size (m x 1) with m=#cols of LS at that time, result stored on each proc.
      _parMatrixOps->multiply(_pseudoInverseChunk[i], negativeResiduals, r_til, colsLSSystemBackThen, getLSSystemRows(), 1);
      // multiply: Wtil^q * r_til  dimensions: (n x m) * (m x 1), fully local and embarrassingly parallel
      xUpdate += _WtilChunk[i] * r_til;
    }
 
    // imvj without restart is used, i.e., compute directly J_prev * (-res)
  } else {
    _parMatrixOps->multiply(_oldInvJacobian, negativeResiduals, xUpdate, _dimOffsets, getLSSystemRows(), getLSSystemRows(), 1, false, false);
    PRECICE_DEBUG("Mult J*V DONE");
  }
 
  xUpdate += xUptmp;
 
  // pending deletion: delete Wtil
  if (_firstIteration && _timeWindowsReused == 0 && not _forceInitialRelaxation) {
    _Wtil.conservativeResize(0, 0);
    _resetLS = true;
  }
}
 
// ==================================================================================
void IQNIMVJAcceleration::computeNewtonUpdate(Eigen::VectorXd &xUpdate)
{
  PRECICE_TRACE();


  Eigen::MatrixXd Z(_qrV.cols(), _qrV.rows());
  pseudoInverse(Z);

  buildWtil();

  _parMatrixOps->multiply(_Wtil, Z, _invJacobian, _dimOffsets, getLSSystemRows(), getLSSystemCols(), getPrimaryLSSystemRows()); // --------
 
  // update Jacobian
  _invJacobian = _invJacobian + _oldInvJacobian;

  Eigen::VectorXd negativeResiduals = -_primaryResiduals;
 
  // multiply J_inv * (-res) = x_Update of dimension: (n x n) * (n x 1) = (n x 1),
  //                                        parallel: (n_global x n_local) * (n_local x 1) = (n_local x 1)
  _parMatrixOps->multiply(_invJacobian, negativeResiduals, xUpdate, _dimOffsets, getLSSystemRows(), getPrimaryLSSystemRows(), 1, false, false); // --------
}
 
// ==================================================================================
void IQNIMVJAcceleration::restartIMVJ()
{
  PRECICE_TRACE();
 
  // int used_storage = 0;
  // int theoreticalJ_storage = 2*getLSSystemRows()*_primaryResiduals.size() + 3*_primaryResiduals.size()*getLSSystemCols() + _primaryResiduals.size()*_primaryResiduals.size();
  //                ------------ RESTART SVD ------------
  if (_imvjRestartType == IQNIMVJAcceleration::RS_SVD) {
 
    // we need to compute the updated SVD of the scaled Jacobian matrix
    // |= APPLY PRECONDITIONING  J_prev = Wtil^q, Z^q  ===|
    for (int i = 0; i < static_cast<int>(_WtilChunk.size()); i++) {
      _preconditioner->apply(_WtilChunk[i]);
      _preconditioner->revert(_pseudoInverseChunk[i], true);
    }
    // |===================                            ===|
 
    Rank rankBefore = _svdJ.isSVDinitialized() ? _svdJ.rank() : 0;
 
    // if it is the first time window, there is no initial SVD, so take all Wtil, Z matrices
    // otherwise, the first element of each container holds the decomposition of the current
    // truncated SVD, i.e., Wtil^0 = \phi, Z^0 = S\psi^T, this should not be added to the SVD.
    int q = _svdJ.isSVDinitialized() ? 1 : 0;
 
    // perform M-1 rank-1 updates of the truncated SVD-dec of the Jacobian
    for (; q < static_cast<int>(_WtilChunk.size()); q++) {
      // update SVD, i.e., PSI * SIGMA * PHI^T <-- PSI * SIGMA * PHI^T + Wtil^q * Z^q
      _svdJ.update(_WtilChunk[q], _pseudoInverseChunk[q].transpose());
      //  used_storage += 2*_WtilChunk.size();
    }
    // int m = _WtilChunk[q].cols(), n = _WtilChunk[q].rows();
    // used_storage += 2*rankBefore*m + 4*m*n + 2*m*m + (rankBefore+m)*(rankBefore+m) + 2*n*(rankBefore+m);
 
    // drop all stored Wtil^q, Z^q matrices
    _WtilChunk.clear();
    _pseudoInverseChunk.clear();
 
    auto &psi   = _svdJ.matrixPsi();
    auto &sigma = _svdJ.singularValues();
    auto &phi   = _svdJ.matrixPhi();
 
    // multiply sigma * phi^T, phi is distributed block-row wise, phi^T is distributed block-column wise
    // sigma is stored local on each proc, thus, the multiplication is fully local, no communication.
    // Z = sigma * phi^T
    Eigen::MatrixXd Z(phi.cols(), phi.rows());
    for (int i = 0; i < static_cast<int>(Z.rows()); i++)
      for (int j = 0; j < static_cast<int>(Z.cols()); j++)
        Z(i, j) = phi(j, i) * sigma[i];
 
    Rank rankAfter = _svdJ.rank();
    int  waste     = _svdJ.getWaste();
    _avgRank += rankAfter;
 
    // store factorized truncated SVD of J
    _WtilChunk.push_back(psi);
    _pseudoInverseChunk.push_back(Z);
 
    // |= REVERT PRECONDITIONING  J_prev = Wtil^0, Z^0  ==|
    _preconditioner->revert(_WtilChunk.front());
    _preconditioner->apply(_pseudoInverseChunk.front(), true);
    // |===================                             ==|
 
    PRECICE_DEBUG("MVJ-RESTART, mode=SVD. Rank of truncated SVD of Jacobian {}, new modes: {}, truncated modes: {} avg rank: {}", rankAfter, rankAfter - rankBefore, waste, _avgRank / _nbRestarts);
 
    // double percentage = 100.0*used_storage/(double)theoreticalJ_storage;
    if (utils::IntraComm::isPrimary() || !utils::IntraComm::isParallel()) {
      _infostringstream << " - MVJ-RESTART " << _nbRestarts << ", mode= SVD -\n  new modes: " << rankAfter - rankBefore << "\n  rank svd: " << rankAfter << "\n  avg rank: " << _avgRank / _nbRestarts << "\n  truncated modes: " << waste << "\n"
                        << '\n';
    }
 
    //        ------------ RESTART LEAST SQUARES ------------
  } else if (_imvjRestartType == IQNIMVJAcceleration::RS_LS) {
    // drop all stored Wtil^q, Z^q matrices
    _WtilChunk.clear();
    _pseudoInverseChunk.clear();
 
    if (_matrixV_RSLS.cols() > 0) {
      // avoid that the system is getting too squared
      while (_matrixV_RSLS.cols() * 2 >= getLSSystemRows()) {
        removeMatrixColumnRSLS(_matrixV_RSLS.cols() - 1);
      }
 
      // preconditioning
      // V needs to be sclaed to compute the pseudo inverse
      // W only needs to be scaled, as the design requires to store scaled
      // matrices Wtil^0 and Z^0 as initial guess after the restart
      _preconditioner->apply(_matrixV_RSLS);
      _preconditioner->apply(_matrixW_RSLS);
 
      impl::QRFactorization qr(_filter);
      qr.setGlobalRows(getPrimaryLSSystemRows());
      // for QR2-filter, the QR-dec is computed in qr-applyFilter()
      if (_filter != Acceleration::QR2FILTER) {
        for (int i = 0; i < static_cast<int>(_matrixV_RSLS.cols()); i++) {
          Eigen::VectorXd v = _matrixV_RSLS.col(i);
          qr.pushBack(v); // same order as matrix V_RSLS
        }
      }
 
      // apply filter
      if (_filter != Acceleration::NOFILTER) {
        std::vector<int> delIndices(0);
        qr.applyFilter(_singularityLimit, delIndices, _matrixV_RSLS);
        // start with largest index (as V,W matrices are shrunk and shifted
        for (int i = delIndices.size() - 1; i >= 0; i--) {
          removeMatrixColumnRSLS(delIndices[i]);
        }
        PRECICE_ASSERT(_matrixV_RSLS.cols() == qr.cols(), _matrixV_RSLS.cols(), qr.cols());
      }

      auto            Q = qr.matrixQ();
      auto            R = qr.matrixR();
      Eigen::MatrixXd pseudoInverse(qr.cols(), qr.rows());
      Eigen::VectorXd yVec(pseudoInverse.rows());
 
      // backsubstitution
      for (int i = 0; i < Q.rows(); i++) {
        Eigen::VectorXd Qrow = Q.row(i);
        yVec                 = R.triangularView<Eigen::Upper>().solve<Eigen::OnTheLeft>(Qrow);
        pseudoInverse.col(i) = yVec;
      }
 
      // scale pseudo inverse back Z := Z' * P,
      // Z' is scaled pseudo inverse i.e, Z' = R^-1 * Q^T * P^-1
      //_preconditioner->apply(pseudoInverse, true, false);
 
      // store factorization of least-squares initial guess for Jacobian
      _WtilChunk.push_back(_matrixW_RSLS);
      _pseudoInverseChunk.push_back(pseudoInverse);
 
      // |= REVERT PRECONDITIONING  J_prev = Wtil^0, Z^0  ==|
      _preconditioner->revert(_WtilChunk.front());
      _preconditioner->apply(_pseudoInverseChunk.front(), true);
      _preconditioner->revert(_matrixW_RSLS);
      _preconditioner->revert(_matrixV_RSLS);
      // |===================                             ==|
    }
 
    PRECICE_DEBUG("MVJ-RESTART, mode=LS. Restart with {} columns from {} time windows.", _matrixV_RSLS.cols(), _RSLSreusedTimeWindows);
    if (utils::IntraComm::isPrimary() || !utils::IntraComm::isParallel()) {
      _infostringstream << " - MVJ-RESTART" << _nbRestarts << ", mode= LS -\n  used cols: " << _matrixV_RSLS.cols() << "\n  R_RS: " << _RSLSreusedTimeWindows << "\n"
                        << '\n';
    }
 
    //            ------------ RESTART ZERO ------------
  } else if (_imvjRestartType == IQNIMVJAcceleration::RS_ZERO) {
    // drop all stored Wtil^q, Z^q matrices
    _WtilChunk.clear();
    _pseudoInverseChunk.clear();
 
    PRECICE_DEBUG("MVJ-RESTART, mode=Zero");
 
  } else if (_imvjRestartType == IQNIMVJAcceleration::RS_SLIDE) {
 
    // re-compute Wtil -- compensate for dropping of Wtil_0 and Z_0:
    //                    Wtil_q <-- Wtil_q +  Wtil^0 * (Z^0*V_q)
    for (int i = static_cast<int>(_WtilChunk.size()) - 1; i >= 1; i--) {
 
      int colsLSSystemBackThen = _pseudoInverseChunk.front().rows();
      PRECICE_ASSERT(colsLSSystemBackThen == _WtilChunk.front().cols(), colsLSSystemBackThen, _WtilChunk.front().cols());
      Eigen::MatrixXd ZV = Eigen::MatrixXd::Zero(colsLSSystemBackThen, _qrV.cols());
      // multiply: ZV := Z^q * V of size (m x m) with m=#cols, stored on each proc.
      _parMatrixOps->multiply(_pseudoInverseChunk.front(), _matrixV, ZV, colsLSSystemBackThen, getLSSystemRows(), _qrV.cols());
      // multiply: Wtil^0 * (Z_0*V)  dimensions: (n x m) * (m x m), fully local and embarrassingly parallel
      Eigen::MatrixXd tmp = Eigen::MatrixXd::Zero(_residuals.rows(), _qrV.cols());
      tmp                 = _WtilChunk.front() * ZV;
      _WtilChunk[i] += tmp;
 
      // drop oldest pair Wtil_0 and Z_0
      PRECICE_ASSERT(not _WtilChunk.empty());
      PRECICE_ASSERT(not _pseudoInverseChunk.empty());
      _WtilChunk.erase(_WtilChunk.begin());
      _pseudoInverseChunk.erase(_pseudoInverseChunk.begin());
    }
 
  } else if (_imvjRestartType == IQNIMVJAcceleration::NO_RESTART) {
    PRECICE_ASSERT(false); // should not happen, in this case _imvjRestart=false
  } else {
    PRECICE_ASSERT(false);
  }
}
 
// ==================================================================================
void IQNIMVJAcceleration::specializedIterationsConverged(
    const DataMap &cplData)
{
  PRECICE_TRACE();
 
  // truncate V_RSLS and W_RSLS matrices according to _RSLSreusedTimeWindows
  if (_imvjRestartType == RS_LS) {
    if (_matrixCols_RSLS.front() == 0) { // Did only one iteration
      _matrixCols_RSLS.pop_front();
    }
    if (_RSLSreusedTimeWindows == 0) {
      _matrixV_RSLS.resize(0, 0);
      _matrixW_RSLS.resize(0, 0);
      _matrixCols_RSLS.clear();
    } else if (static_cast<int>(_matrixCols_RSLS.size()) > _RSLSreusedTimeWindows) {
      int toRemove = _matrixCols_RSLS.back();
      PRECICE_ASSERT(toRemove > 0, toRemove);
      if (_matrixV_RSLS.size() > 0) {
        PRECICE_ASSERT(_matrixV_RSLS.cols() > toRemove, _matrixV_RSLS.cols(), toRemove);
      }
 
      // remove columns
      for (int i = 0; i < toRemove; i++) {
        utils::removeColumnFromMatrix(_matrixV_RSLS, _matrixV_RSLS.cols() - 1);
        utils::removeColumnFromMatrix(_matrixW_RSLS, _matrixW_RSLS.cols() - 1);
      }
      _matrixCols_RSLS.pop_back();
    }
    _matrixCols_RSLS.push_front(0);
  }
 
  //_info2<<'\n';
 
  // if efficient update of imvj is enabled
  if (not _alwaysBuildJacobian || _imvjRestart) {
    // need to apply the preconditioner, as all data structures are reverted after
    // call to computeQNUpdate. Need to call this before the preconditioner is updated.
 
    // |= REBUILD QR-dec if needed     ============|
    // apply scaling to V, V' := P * V (only needed to reset the QR-dec of V)
    _preconditioner->apply(_matrixV);
 
    if (_preconditioner->requireNewQR()) {
      if (not(_filter == Acceleration::QR2FILTER)) { // for QR2 filter, there is no need to do this twice
        _qrV.reset(_matrixV, getLSSystemRows());
      }
      _preconditioner->newQRfulfilled();
    }
    // apply the configured filter to the LS system
    // as it changed in BaseQNAcceleration::iterationsConverged()
    BaseQNAcceleration::applyFilter();
    _preconditioner->revert(_matrixV);
    // |===================          ============|
 
    //              ------- RESTART/ JACOBIAN ASSEMBLY -------
    if (_imvjRestart) {
 
      // add the matrices Wtil and Z of the converged configuration to the storage containers
      Eigen::MatrixXd Z(_qrV.cols(), _qrV.rows());
      // compute pseudo inverse using QR factorization and back-substitution
      // also compensates for the scaling of V, i.e.,
      // reverts Z' = R^-1 * Q^T * P^-1 as Z := Z' * P
      pseudoInverse(Z);
 
      // push back unscaled pseudo Inverse, Wtil is also unscaled.
      // all objects in Wtil chunk and Z chunk are NOT PRECONDITIONED
      _WtilChunk.push_back(_Wtil);
      _pseudoInverseChunk.push_back(Z);

      if (static_cast<int>(_WtilChunk.size()) >= _chunkSize + 1) {
 
        // < RESTART >
        _nbRestarts++;
        profiling::Event restartUpdate("IMVJRestart");
        restartIMVJ();
        restartUpdate.stop();
      }
 
      // only in imvj normal mode with efficient update:
    } else {
 
      // compute explicit representation of Jacobian
      buildJacobian();
    }

    if (_timeWindowsReused > 0 || (_timeWindowsReused == 0 && _forceInitialRelaxation)) {
      //_Wtil.conservativeResize(0, 0);
      _resetLS = true;
    }
  }
 
  // only store Jacobian if imvj is in normal mode, i.e., the Jacobian is build
  if (not _imvjRestart) {
    // store inverse Jacobian from converged time window. NOT SCALED with preconditioner
    _oldInvJacobian = _invJacobian;
  }
}
 
// ==================================================================================
void IQNIMVJAcceleration::removeMatrixColumn(
    int columnIndex)
{
  PRECICE_TRACE(columnIndex, _matrixV.cols());
  PRECICE_ASSERT(_matrixV.cols() > 1, _matrixV.cols());
  PRECICE_ASSERT(_Wtil.cols() > 1);
 
  // remove column from matrix _Wtil
  if (not _resetLS && not _alwaysBuildJacobian)
    utils::removeColumnFromMatrix(_Wtil, columnIndex);
 
  BaseQNAcceleration::removeMatrixColumn(columnIndex);
}
 
void IQNIMVJAcceleration::specializedInitializeVectorsAndPreconditioner(const DataMap &cplData)
{
  int entries        = _primaryResiduals.size();
  int cplDataEntries = _residuals.size();
  int global_n       = 0;
 
  if (!utils::IntraComm::isParallel()) {
    global_n = cplDataEntries;
  } else {
    global_n = _dimOffsets.back();
  }
 
  // initialize parallel matrix-matrix operation module
  _parMatrixOps = std::make_shared<impl::ParallelMatrixOperations>();
  _parMatrixOps->initialize(not _imvjRestart);
  _svdJ.initialize(_parMatrixOps, global_n, getLSSystemRows());
 
  if (not _imvjRestart) {
    // only need memory for Jacobain if not in restart mode
    _invJacobian    = Eigen::MatrixXd::Zero(global_n, entries);
    _oldInvJacobian = Eigen::MatrixXd::Zero(global_n, entries);
  }
 
  // initialize parallel matrix-matrix operation module
  _parMatrixOps = std::make_shared<impl::ParallelMatrixOperations>();
  _parMatrixOps->initialize(not _imvjRestart);
  _svdJ.reset();
  _svdJ.initialize(_parMatrixOps, global_n, getLSSystemRows());
 
  // initialize V, W matrices for the LS restart
  if (_imvjRestartType == RS_LS) {
    _matrixCols_RSLS.clear();
    _matrixCols_RSLS.push_front(0);
    _matrixV_RSLS = Eigen::MatrixXd::Zero(entries, 0);
    _matrixW_RSLS = Eigen::MatrixXd::Zero(cplDataEntries, 0);
  }
  _Wtil = Eigen::MatrixXd::Zero(cplDataEntries, 0);
  if (_imvjRestartType > 0) {
    // drop all stored Wtil^q, Z^q matrices
    _WtilChunk.clear();
    _pseudoInverseChunk.clear();
  }
 
  if (utils::IntraComm::isPrimary() || !utils::IntraComm::isParallel()) {
    _infostringstream << " IMVJ restart mode: " << _imvjRestart << "\n chunk size: " << _chunkSize << "\n trunc eps: " << _svdJ.getThreshold() << "\n R_RS: " << _RSLSreusedTimeWindows << "\n--------\n"
                      << '\n';
  }
}
 
// ==================================================================================
void IQNIMVJAcceleration::removeMatrixColumnRSLS(
    int columnIndex)
{
  PRECICE_TRACE(columnIndex, _matrixV_RSLS.cols());
  PRECICE_ASSERT(_matrixV_RSLS.cols() > 1);
 
  utils::removeColumnFromMatrix(_matrixV_RSLS, columnIndex);
  utils::removeColumnFromMatrix(_matrixW_RSLS, columnIndex);
 
  // Reduce column count
  std::deque<int>::iterator iter = _matrixCols_RSLS.begin();
  int                       cols = 0;
  while (iter != _matrixCols_RSLS.end()) {
    cols += *iter;
    if (cols > columnIndex) {
      PRECICE_ASSERT(*iter > 0);
      *iter -= 1;
      if (*iter == 0) {
        _matrixCols_RSLS.erase(iter);
      }
      break;
    }
    iter++;
  }
}
 
} // namespace precice::acceleration
 
#endif