MueLu_AggregateQualityEstimateFactory_def.hpp

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#ifndef MUELU_AGGREGATEQUALITYESTIMATEFACTORY_DEF_HPP
#define MUELU_AGGREGATEQUALITYESTIMATEFACTORY_DEF_HPP
#include <iomanip>
#include "MueLu_AggregateQualityEstimateFactory_decl.hpp"
 
#include "MueLu_Level.hpp"
 
#include <Teuchos_SerialDenseVector.hpp>
#include <Teuchos_LAPACK.hpp>
 
#include "MueLu_Aggregates_decl.hpp"
#include <Xpetra_IO.hpp>
#include "MueLu_MasterList.hpp"
#include "MueLu_Monitor.hpp"
#include "MueLu_Utilities.hpp"
 
#include <vector>
 
namespace MueLu {
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::AggregateQualityEstimateFactory()
  { }
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::~AggregateQualityEstimateFactory() {}
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::DeclareInput(Level& currentLevel) const {
 
    Input(currentLevel, "A");
    Input(currentLevel, "Aggregates");
    Input(currentLevel, "CoarseMap");
 
  }
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  RCP<const ParameterList> AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::GetValidParameterList() const {
    RCP<ParameterList> validParamList = rcp(new ParameterList());
 
#define SET_VALID_ENTRY(name) validParamList->setEntry(name, MasterList::getEntry(name))
    SET_VALID_ENTRY("aggregate qualities: good aggregate threshold");
    SET_VALID_ENTRY("aggregate qualities: file output");
    SET_VALID_ENTRY("aggregate qualities: file base");
    SET_VALID_ENTRY("aggregate qualities: check symmetry");
    SET_VALID_ENTRY("aggregate qualities: algorithm");
    SET_VALID_ENTRY("aggregate qualities: zero threshold");
    SET_VALID_ENTRY("aggregate qualities: percentiles");
    SET_VALID_ENTRY("aggregate qualities: mode");
 
#undef  SET_VALID_ENTRY
 
    validParamList->set< RCP<const FactoryBase> >("A",                  Teuchos::null, "Generating factory of the matrix A");
    validParamList->set< RCP<const FactoryBase> >("Aggregates",         Teuchos::null, "Generating factory of the aggregates");
    validParamList->set< RCP<const FactoryBase> >("CoarseMap",          Teuchos::null, "Generating factory of the coarse map");
 
    return validParamList;
  }
 
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::Build(Level & currentLevel) const {
 
    FactoryMonitor m(*this, "Build", currentLevel);
 
    RCP<Matrix> A = Get<RCP<Matrix>>(currentLevel, "A");
    RCP<Aggregates> aggregates = Get<RCP<Aggregates>>(currentLevel, "Aggregates");
 
    RCP<const Map> map = Get< RCP<const Map> >(currentLevel, "CoarseMap");
 
 
    assert(!aggregates->AggregatesCrossProcessors());
    ParameterList pL = GetParameterList();
    std::string mode = pL.get<std::string>("aggregate qualities: mode");
    GetOStream(Statistics1) << "AggregateQuality: mode "<<mode << std::endl;
 
    RCP<Xpetra::MultiVector<magnitudeType,LO,GO,NO>> aggregate_qualities;
    if(mode == "eigenvalue" || mode == "both") {
      aggregate_qualities = Xpetra::MultiVectorFactory<magnitudeType,LO,GO,NO>::Build(map, 1);
      ComputeAggregateQualities(A, aggregates, aggregate_qualities);
      OutputAggQualities(currentLevel, aggregate_qualities);
    }
    if(mode == "size" || mode =="both") {
      RCP<LocalOrdinalVector> aggregate_sizes = Xpetra::VectorFactory<LO,LO,GO,NO>::Build(map);
      ComputeAggregateSizes(A,aggregates,aggregate_sizes);
      Set(currentLevel, "AggregateSizes",aggregate_sizes);
      OutputAggSizes(currentLevel, aggregate_sizes);
    }
      Set(currentLevel, "AggregateQualities", aggregate_qualities);
 
 
  }
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::ConvertAggregatesData(RCP<const Aggregates> aggs, ArrayRCP<LO>& aggSortedVertices, ArrayRCP<LO>& aggsToIndices, ArrayRCP<LO>& aggSizes) {
 
    // Reorder local aggregate information into a format amenable to computing
    // per-aggregate quantities. Specifically, we compute a format
    // similar to compressed sparse row format for sparse matrices in which
    // we store all the local vertices in a single array in blocks corresponding
    // to aggregates. (This array is aggSortedVertices.) We then store a second
    // array (aggsToIndices) whose k-th element stores the index of the first
    // vertex in aggregate k in the array aggSortedVertices.
 
    const LO LO_ZERO = Teuchos::OrdinalTraits<LO>::zero();
    const LO LO_ONE = Teuchos::OrdinalTraits<LO>::one();
 
    LO numAggs = aggs->GetNumAggregates();
    aggSizes = aggs->ComputeAggregateSizesArrayRCP();
 
    aggsToIndices = ArrayRCP<LO>(numAggs+LO_ONE,LO_ZERO);
 
    for (LO i=0;i<numAggs;++i) {
      aggsToIndices[i+LO_ONE] = aggsToIndices[i] + aggSizes[i];
    }
 
    const RCP<LOMultiVector> vertex2AggId = aggs->GetVertex2AggId();
    const ArrayRCP<const LO> vertex2AggIdData = vertex2AggId->getData(0);
 
    LO numNodes = vertex2AggId->getLocalLength();
    aggSortedVertices = ArrayRCP<LO>(numNodes,-LO_ONE);
    std::vector<LO> vertexInsertionIndexByAgg(numNodes,LO_ZERO);
 
    for (LO i=0;i<numNodes;++i) {
 
      LO aggId = vertex2AggIdData[i];
      if (aggId<0 || aggId>=numAggs) continue;
 
      aggSortedVertices[aggsToIndices[aggId]+vertexInsertionIndexByAgg[aggId]] = i;
      vertexInsertionIndexByAgg[aggId]++;
 
    }
 
 
  }
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::ComputeAggregateQualities(RCP<const Matrix> A, RCP<const Aggregates> aggs, RCP<Xpetra::MultiVector<magnitudeType,LO,GO,Node>> agg_qualities) const {
 
    const SC SCALAR_ONE = Teuchos::ScalarTraits<SC>::one();
    const SC SCALAR_TWO = SCALAR_ONE + SCALAR_ONE;
 
    const LO LO_ZERO = Teuchos::OrdinalTraits<LO>::zero();
    const LO LO_ONE = Teuchos::OrdinalTraits<LO>::one();
 
    using MT = magnitudeType;
    const MT MT_ZERO = Teuchos::ScalarTraits<MT>::zero();
    const MT MT_ONE = Teuchos::ScalarTraits<MT>::one();
    ParameterList pL = GetParameterList();
 
    RCP<const Matrix> AT = A;
 
    // Algorithm check
    std::string algostr = pL.get<std::string>("aggregate qualities: algorithm");
    MT zeroThreshold = Teuchos::as<MT>(pL.get<double>("aggregate qualities: zero threshold"));
    enum AggAlgo {ALG_FORWARD=0, ALG_REVERSE};
    AggAlgo algo;
    if(algostr == "forward")      {algo = ALG_FORWARD; GetOStream(Statistics1) << "AggregateQuality: Using 'forward' algorithm" << std::endl;}
    else if(algostr == "reverse") {algo = ALG_REVERSE; GetOStream(Statistics1) << "AggregateQuality: Using 'reverse' algorithm" << std::endl;}
    else {
      TEUCHOS_TEST_FOR_EXCEPTION(1, Exceptions::RuntimeError, "\"algorithm\" must be one of (forward|reverse)");
    }
 
    bool check_symmetry = pL.get<bool>("aggregate qualities: check symmetry");
    if (check_symmetry) {
 
      RCP<MultiVector> x = MultiVectorFactory::Build(A->getMap(), 1, false);
      x->Xpetra_randomize();
 
      RCP<MultiVector> tmp = MultiVectorFactory::Build(A->getMap(), 1, false);
 
      A->apply(*x, *tmp, Teuchos::NO_TRANS); // tmp now stores A*x
      A->apply(*x, *tmp, Teuchos::TRANS, -SCALAR_ONE, SCALAR_ONE); // tmp now stores A*x - A^T*x
 
      Array<magnitudeType> tmp_norm(1);
      tmp->norm2(tmp_norm());
 
      Array<magnitudeType> x_norm(1);
      tmp->norm2(x_norm());
 
      if (tmp_norm[0] > 1e-10*x_norm[0]) {
        std::string transpose_string = "transpose";
        RCP<ParameterList> whatever;
        AT = Utilities::Transpose(*rcp_const_cast<Matrix>(A), true, transpose_string, whatever);
 
        assert(A->getMap()->isSameAs( *(AT->getMap()) ));
      }
 
    }
 
    // Reorder local aggregate information into a format amenable to computing
    // per-aggregate quantities. Specifically, we compute a format
    // similar to compressed sparse row format for sparse matrices in which
    // we store all the local vertices in a single array in blocks corresponding
    // to aggregates. (This array is aggSortedVertices.) We then store a second
    // array (aggsToIndices) whose k-th element stores the index of the first
    // vertex in aggregate k in the array aggSortedVertices.
 
    ArrayRCP<LO> aggSortedVertices, aggsToIndices, aggSizes;
    ConvertAggregatesData(aggs, aggSortedVertices, aggsToIndices, aggSizes);
 
    LO numAggs = aggs->GetNumAggregates();
 
    // Compute the per-aggregate quality estimate
 
    typedef Teuchos::SerialDenseMatrix<LO,MT> DenseMatrix;
    typedef Teuchos::SerialDenseVector<LO,MT> DenseVector;
 
    ArrayView<const LO> rowIndices;
    ArrayView<const SC> rowValues;
    ArrayView<const SC> colValues;
    Teuchos::LAPACK<LO,MT> myLapack;
 
    // Iterate over each aggregate to compute the quality estimate
    for (LO aggId=LO_ZERO; aggId<numAggs; ++aggId) {
 
      LO aggSize = aggSizes[aggId];
      DenseMatrix A_aggPart(aggSize, aggSize, true);
      DenseVector offDiagonalAbsoluteSums(aggSize, true);
 
      // Iterate over each node in the aggregate
      for (LO idx=LO_ZERO; idx<aggSize; ++idx) {
 
        LO nodeId = aggSortedVertices[idx+aggsToIndices[aggId]];
        A->getLocalRowView(nodeId, rowIndices, rowValues);
        AT->getLocalRowView(nodeId, rowIndices, colValues);
 
        // Iterate over each element in the row corresponding to the current node
        for (LO elem=LO_ZERO; elem<rowIndices.size();++elem) {
 
          LO nodeId2 = rowIndices[elem];
          SC val = (rowValues[elem]+colValues[elem])/SCALAR_TWO;
 
          LO idxInAgg = -LO_ONE; // -1 if element is not in aggregate
 
          // Check whether the element belongs in the aggregate. If it does
          // find, its index. Otherwise, add it's value to the off diagonal
          // sums
          for (LO idx2=LO_ZERO; idx2<aggSize; ++idx2) {
 
            if (aggSortedVertices[idx2+aggsToIndices[aggId]] == nodeId2) {
 
              // Element does belong to aggregate
              idxInAgg = idx2;
              break;
 
            }
 
          }
 
          if (idxInAgg == -LO_ONE) { // Element does not belong to aggregate
 
            offDiagonalAbsoluteSums[idx] += Teuchos::ScalarTraits<SC>::magnitude(val);
 
          } else { // Element does belong to aggregate
 
            A_aggPart(idx,idxInAgg) = Teuchos::ScalarTraits<SC>::real(val);
 
          }
 
        }
 
      }
 
      // Construct a diagonal matrix consisting of the diagonal
      // of A_aggPart
      DenseMatrix A_aggPartDiagonal(aggSize, aggSize, true);
      MT diag_sum = MT_ZERO;
      for (int i=0;i<aggSize;++i) {
        A_aggPartDiagonal(i,i) = Teuchos::ScalarTraits<SC>::real(A_aggPart(i,i));
        diag_sum += Teuchos::ScalarTraits<SC>::real(A_aggPart(i,i));
      }
 
      DenseMatrix ones(aggSize, aggSize, false);
      ones.putScalar(MT_ONE);
 
      // Compute matrix on top of generalized Rayleigh quotient
      // topMatrix = A_aggPartDiagonal - A_aggPartDiagonal*ones*A_aggPartDiagonal/diag_sum;
      DenseMatrix tmp(aggSize, aggSize, false);
      DenseMatrix topMatrix(A_aggPartDiagonal);
 
      tmp.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, MT_ONE, ones, A_aggPartDiagonal, MT_ZERO);
      topMatrix.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, -MT_ONE/diag_sum, A_aggPartDiagonal, tmp, MT_ONE);
 
      // Compute matrix on bottom of generalized Rayleigh quotient
      DenseMatrix bottomMatrix(A_aggPart);
      MT matrixNorm = A_aggPart.normInf();
 
      // Forward mode: Include a small perturbation to the bottom matrix to make it nonsingular
      const MT boost = (algo == ALG_FORWARD) ? (-1e4*Teuchos::ScalarTraits<MT>::eps()*matrixNorm) : MT_ZERO;
 
      for (int i=0;i<aggSize;++i){
        bottomMatrix(i,i) -= offDiagonalAbsoluteSums(i) + boost;
      }
 
      // Compute generalized eigenvalues
      LO sdim, info;
      DenseVector alpha_real(aggSize, false);
      DenseVector alpha_imag(aggSize, false);
      DenseVector beta(aggSize, false);
 
      DenseVector workArray(14*(aggSize+1), false);
 
      LO (*ptr2func)(MT*, MT*, MT*);
      ptr2func = NULL;
      LO* bwork = NULL;
      MT* vl = NULL;
      MT* vr = NULL;
 
      const char compute_flag ='N';
      if(algo == ALG_FORWARD) {
        // Forward: Solve the generalized eigenvalue problem as is
        myLapack.GGES(compute_flag,compute_flag,compute_flag,ptr2func,aggSize,
                      topMatrix.values(),aggSize,bottomMatrix.values(),aggSize,&sdim,
                      alpha_real.values(),alpha_imag.values(),beta.values(),vl,aggSize,
                      vr,aggSize,workArray.values(),workArray.length(),bwork,
                      &info);
        TEUCHOS_ASSERT(info == LO_ZERO);
 
        MT maxEigenVal = MT_ZERO;
        for (int i=LO_ZERO;i<aggSize;++i) {
          // NOTE: In theory, the eigenvalues should be nearly real
          //TEUCHOS_ASSERT(fabs(alpha_imag[i]) <= 1e-8*fabs(alpha_real[i])); // Eigenvalues should be nearly real
          maxEigenVal = std::max(maxEigenVal, alpha_real[i]/beta[i]);
        }
        
        (agg_qualities->getDataNonConst(0))[aggId] = (MT_ONE+MT_ONE)*maxEigenVal;
      }
      else {
        // Reverse: Swap the top and bottom matrices for the generalized eigenvalue problem
        // This is trickier, since we need to grab the smallest non-zero eigenvalue and invert it.
        myLapack.GGES(compute_flag,compute_flag,compute_flag,ptr2func,aggSize,
                      bottomMatrix.values(),aggSize,topMatrix.values(),aggSize,&sdim,
                      alpha_real.values(),alpha_imag.values(),beta.values(),vl,aggSize,
                      vr,aggSize,workArray.values(),workArray.length(),bwork,
                      &info);
 
        TEUCHOS_ASSERT(info == LO_ZERO);
        
        MT minEigenVal = MT_ZERO;
       
        for (int i=LO_ZERO;i<aggSize;++i) {
          MT ev = alpha_real[i] / beta[i];
          if(ev > zeroThreshold) {
            if (minEigenVal == MT_ZERO) minEigenVal = ev;
            else minEigenVal = std::min(minEigenVal,ev);
          }
        }
        if(minEigenVal == MT_ZERO)  (agg_qualities->getDataNonConst(0))[aggId] = Teuchos::ScalarTraits<MT>::rmax();
        else (agg_qualities->getDataNonConst(0))[aggId] = (MT_ONE+MT_ONE) / minEigenVal;
      }
    }//end aggId loop
  }
 
  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::OutputAggQualities(const Level& level, RCP<const Xpetra::MultiVector<magnitudeType,LO,GO,Node>> agg_qualities) const {
 
    ParameterList pL = GetParameterList();
 
    magnitudeType good_agg_thresh = Teuchos::as<magnitudeType>(pL.get<double>("aggregate qualities: good aggregate threshold"));
    using MT = magnitudeType;
 
    ArrayRCP<const MT> data = agg_qualities->getData(0);
 
    LO num_bad_aggs = 0;
    MT worst_agg = 0.0;
 
    MT mean_bad_agg = 0.0;
    MT mean_good_agg  = 0.0;
 
 
    for (size_t i=0;i<agg_qualities->getLocalLength();++i) {
 
      if (data[i] > good_agg_thresh) {
        num_bad_aggs++;
        mean_bad_agg += data[i];
      }
      else {
        mean_good_agg += data[i];
      }
      worst_agg = std::max(worst_agg, data[i]);
    }
 
 
    if (num_bad_aggs > 0) mean_bad_agg /= num_bad_aggs;
    mean_good_agg /= agg_qualities->getLocalLength() - num_bad_aggs;
 
    if (num_bad_aggs == 0) {
      GetOStream(Statistics1) << "All aggregates passed the quality measure. Worst aggregate had quality " << worst_agg << ". Mean aggregate quality " << mean_good_agg << "." << std::endl;
    } else {
      GetOStream(Statistics1) << num_bad_aggs << " out of " << agg_qualities->getLocalLength() << " did not pass the quality measure. Worst aggregate had quality " << worst_agg << ". "
                              << "Mean bad aggregate quality " << mean_bad_agg << ". Mean good aggregate quality " << mean_good_agg << "." << std::endl;
    }
 
    if (pL.get<bool>("aggregate qualities: file output")) {
      std::string filename = pL.get<std::string>("aggregate qualities: file base")+"."+std::to_string(level.GetLevelID());
      Xpetra::IO<magnitudeType,LO,GO,Node>::Write(filename, *agg_qualities);
    }
 
    {
      const auto n = size_t(agg_qualities->getLocalLength());
 
      std::vector<MT> tmp;
      tmp.reserve(n);
 
      for (size_t i=0; i<n; ++i) {
        tmp.push_back(data[i]);
      }
 
      std::sort(tmp.begin(), tmp.end());
 
      Teuchos::ArrayView<const double> percents = pL.get<Teuchos::Array<double> >("aggregate qualities: percentiles")();
 
      GetOStream(Statistics1) << "AGG QUALITY HEADER     : | LEVEL |  TOTAL  |";
      for (auto percent : percents) {
        GetOStream(Statistics1) << std::fixed << std::setprecision(4) <<100.0*percent << "% |";
      }
      GetOStream(Statistics1) << std::endl;
 
      GetOStream(Statistics1) << "AGG QUALITY PERCENTILES: | " << level.GetLevelID() << " | " << n << "|";
      for (auto percent : percents) {
        size_t i = size_t(n*percent);
        i = i < n ? i : n-1u;
        i = i > 0u ? i : 0u;
        GetOStream(Statistics1) << std::fixed <<std::setprecision(4) << tmp[i] << " |";
      }
      GetOStream(Statistics1) << std::endl;
 
    }
  }
 
 
 
template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::ComputeAggregateSizes(RCP<const Matrix> A, RCP<const Aggregates> aggs, RCP<LocalOrdinalVector> agg_sizes) const {
 
    ArrayRCP<LO> aggSortedVertices, aggsToIndices, aggSizes;
    ConvertAggregatesData(aggs, aggSortedVertices, aggsToIndices, aggSizes);
 
    // Iterate over each node in the aggregate
    auto data = agg_sizes->getDataNonConst(0);
    for (LO i=0; i<(LO)aggSizes.size(); i++)
      data[i] = aggSizes[i];
}
 
 
 
template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregateQualityEstimateFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::OutputAggSizes(const Level& level, RCP<const LocalOrdinalVector> agg_sizes) const {
 
    ParameterList pL = GetParameterList();
    using MT = magnitudeType;
 
    ArrayRCP<const LO> data = agg_sizes->getData(0);
 
 
    if (pL.get<bool>("aggregate qualities: file output")) {
      std::string filename = pL.get<std::string>("aggregate qualities: file base")+".sizes."+std::to_string(level.GetLevelID());
      Xpetra::IO<LO,LO,GO,Node>::Write(filename, *agg_sizes);
    }
 
    {
      size_t n = (size_t)agg_sizes->getLocalLength();
 
      std::vector<MT> tmp;
      tmp.reserve(n);
 
      for (size_t i=0; i<n; ++i) {
        tmp.push_back(Teuchos::as<MT>(data[i]));
      }
 
      std::sort(tmp.begin(), tmp.end());
 
      Teuchos::ArrayView<const double> percents = pL.get<Teuchos::Array<double> >("aggregate qualities: percentiles")();
 
      GetOStream(Statistics1) << "AGG SIZE    HEADER     : | LEVEL |  TOTAL  |";
      for (auto percent : percents) {
        GetOStream(Statistics1) << std::fixed << std::setprecision(4) <<100.0*percent << "% |";
      }
      GetOStream(Statistics1) << std::endl;
 
      GetOStream(Statistics1) << "AGG SIZE    PERCENTILES: | " << level.GetLevelID() << " | " << n << "|";
      for (auto percent : percents) {
        size_t i = size_t(n*percent);
        i = i < n ? i : n-1u;
        i = i > 0u ? i : 0u;
        GetOStream(Statistics1) << std::fixed <<std::setprecision(4) << tmp[i] << " |";
      }
      GetOStream(Statistics1) << std::endl;
 
    }
  }
 
 
 
} // namespace MueLu
 
#endif // MUELU_AGGREGATEQUALITYESTIMATE_DEF_HPP