BelosThyraAdapter.hpp

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//                 Belos: Block Eigensolvers Package
//                 Copyright (2004) Sandia Corporation
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#ifndef BELOS_THYRA_ADAPTER_HPP
#define BELOS_THYRA_ADAPTER_HPP

#include "BelosMultiVecTraits.hpp"
#include "BelosOperatorTraits.hpp"
#include "BelosConfigDefs.hpp"

#include <Thyra_DetachedMultiVectorView.hpp>
#include <Thyra_MultiVectorBaseDecl.hpp>

namespace Belos {
  
  //
  // Implementation of the Belos::MultiVecTraits for Thyra::MultiVectorBase
  //

  template<class ScalarType>
  class MultiVecTraits< ScalarType, Thyra::MultiVectorBase<ScalarType> >
  {
  private:
    typedef Thyra::MultiVectorBase<ScalarType> TMVB;
    typedef Teuchos::ScalarTraits<ScalarType> ST;
    typedef typename ST::magnitudeType magType;
  public:


    static Teuchos::RCP<TMVB> Clone( const TMVB& mv, const int numvecs )
    { 
      Teuchos::RCP<TMVB> c = Thyra::createMembers( mv.range(), numvecs ); 
      return c;
    }

    static Teuchos::RCP<TMVB> CloneCopy( const TMVB& mv )
    { 
      int numvecs = mv.domain()->dim();
      // create the new multivector
      Teuchos::RCP< TMVB > cc = Thyra::createMembers( mv.range(), numvecs );
      // copy the data from the source multivector to the new multivector
      Thyra::assign(&*cc, mv);
      return cc;
    }

    static Teuchos::RCP<TMVB> CloneCopy( const TMVB& mv, const std::vector<int>& index )
    { 
      int numvecs = index.size();
      // create the new multivector
      Teuchos::RCP< TMVB > cc = Thyra::createMembers( mv.range(), numvecs );
      // create a view to the relevant part of the source multivector
      Teuchos::RCP< const TMVB > view = mv.subView( numvecs, &(index[0]) );
      // copy the data from the relevant view to the new multivector
      Thyra::assign(&*cc, *view);
      return cc;
    }

    static Teuchos::RCP<TMVB> CloneView( TMVB& mv, const std::vector<int>& index )
    {
      int numvecs = index.size();

      // We do not assume that the indices are sorted, nor do we check that
      // index.size() > 0. This code is fail-safe, in the sense that a zero
      // length index std::vector will pass the error on the Thyra.

      // Thyra has two ways to create an indexed View:
      // * contiguous (via a range of columns)
      // * indexed (via a std::vector of column indices)
      // The former is significantly more efficient than the latter, in terms of
      // computations performed with/against the created view.
      // We will therefore check to see if the given indices are contiguous, and
      // if so, we will use the contiguous view creation method.

      int lb = index[0];
      bool contig = true;
      for (int i=0; i<numvecs; i++) {
        if (lb+i != index[i]) contig = false;
      }

      Teuchos::RCP< TMVB > cc;
      if (contig) {
        const Thyra::Range1D rng(lb,lb+numvecs-1);
        // create a contiguous view to the relevant part of the source multivector
        cc = mv.subView(rng);
      }
      else {
        // create an indexed view to the relevant part of the source multivector
        cc = mv.subView( numvecs, &(index[0]) );
      }
      return cc;
    }

    static Teuchos::RCP<const TMVB> CloneView( const TMVB& mv, const std::vector<int>& index )
    {
      int numvecs = index.size();

      // We do not assume that the indices are sorted, nor do we check that
      // index.size() > 0. This code is fail-safe, in the sense that a zero
      // length index std::vector will pass the error on the Thyra.

      // Thyra has two ways to create an indexed View:
      // * contiguous (via a range of columns)
      // * indexed (via a std::vector of column indices)
      // The former is significantly more efficient than the latter, in terms of
      // computations performed with/against the created view.
      // We will therefore check to see if the given indices are contiguous, and
      // if so, we will use the contiguous view creation method.

      int lb = index[0];
      bool contig = true;
      for (int i=0; i<numvecs; i++) {
        if (lb+i != index[i]) contig = false;
      }

      Teuchos::RCP< const TMVB > cc;
      if (contig) {
        const Thyra::Range1D rng(lb,lb+numvecs-1);
        // create a contiguous view to the relevant part of the source multivector
        cc = mv.subView(rng);
      }
      else {
        // create an indexed view to the relevant part of the source multivector
        cc = mv.subView( numvecs, &(index[0]) );
      }
      return cc;
    }



    static int GetVecLength( const TMVB& mv )
    { return mv.range()->dim(); }

    static int GetNumberVecs( const TMVB& mv )
    { return mv.domain()->dim(); }



    static void MvTimesMatAddMv( const ScalarType alpha, const TMVB& A, 
         const Teuchos::SerialDenseMatrix<int,ScalarType>& B, 
         const ScalarType beta, TMVB& mv )
    {
      int m = B.numRows();
      int n = B.numCols();
      // Create a view of the B object!
      Teuchos::RCP< const TMVB >
        B_thyra = Thyra::createMembersView(
          A.domain()
          ,RTOpPack::ConstSubMultiVectorView<ScalarType>(0,m,0,n,&B(0,0),B.stride())
          );
      // perform the operation via A: mv <- alpha*A*B_thyra + beta*mv
      A.apply(Thyra::NONCONJ_ELE,*B_thyra,&mv,alpha,beta);
    }

    static void MvAddMv( const ScalarType alpha, const TMVB& A, 
                         const ScalarType beta,  const TMVB& B, TMVB& mv )
    { 
      ScalarType coef[2], zero = Teuchos::ScalarTraits<ScalarType>::zero();
      const TMVB * in[2];

      in[0] = &A;
      in[1] = &B;
      coef[0] = alpha;
      coef[1] = beta;

      Thyra::linear_combination(2,coef,in,zero,&mv);
    }

    static void MvScale ( TMVB& mv, const ScalarType alpha )
    { Thyra::scale(alpha,&mv); }

    static void MvScale ( TMVB& mv, const std::vector<ScalarType>& alpha )
    {
      for (unsigned int i=0; i<alpha.size(); i++) {
        Thyra::scale(alpha[i],mv.col(i).get());
      }
    }

    static void MvTransMv( const ScalarType alpha, const TMVB& A, const TMVB& mv, 
                           Teuchos::SerialDenseMatrix<int,ScalarType>& B )
    { 
      // Create a multivector to hold the result (m by n)
      int m = A.domain()->dim();
      int n = mv.domain()->dim();
      // Create a view of the B object!
      Teuchos::RCP< TMVB >
        B_thyra = Thyra::createMembersView(
          A.domain()
          ,RTOpPack::SubMultiVectorView<ScalarType>(0,m,0,n,&B(0,0),B.stride())
          );
      A.applyTranspose(Thyra::CONJ_ELE,mv,&*B_thyra,alpha,Teuchos::ScalarTraits<ScalarType>::zero());
    }

    static void MvDot( const TMVB& mv, const TMVB& A, std::vector<ScalarType>& b )
    { Thyra::dots(mv,A,&(b[0])); }



    static void MvNorm( const TMVB& mv, std::vector<magType>& normvec, NormType type = TwoNorm )
    { Thyra::norms_2(mv,&(normvec[0])); }



    static void SetBlock( const TMVB& A, const std::vector<int>& index, TMVB& mv )
    { 
      // Extract the "numvecs" columns of mv indicated by the index std::vector.
      int numvecs = index.size();
      std::vector<int> indexA(numvecs);
      int numAcols = A.domain()->dim();
      for (int i=0; i<numvecs; i++) {
        indexA[i] = i;
      }
      // Thyra::assign requires that both arguments have the same number of 
      // vectors. Enforce this, by shrinking one to match the other.
      if ( numAcols < numvecs ) {
        // A does not have enough columns to satisfy index_plus. Shrink
        // index_plus.
        numvecs = numAcols;
      }
      else if ( numAcols > numvecs ) {
        numAcols = numvecs;
        indexA.resize( numAcols );
      }
      // create a view to the relevant part of the source multivector
      Teuchos::RCP< const TMVB > relsource = A.subView( numAcols, &(indexA[0]) );
      // create a view to the relevant part of the destination multivector
      Teuchos::RCP< TMVB > reldest = mv.subView( numvecs, &(index[0]) );
      // copy the data to the destination multivector subview
      Thyra::assign(&*reldest, *relsource);
    }

    static void MvRandom( TMVB& mv )
    { 
      // Thyra::randomize generates via a uniform distribution on [l,u]
      // We will use this to generate on [-1,1]
      Thyra::randomize(-Teuchos::ScalarTraits<ScalarType>::one(),
                        Teuchos::ScalarTraits<ScalarType>::one(),
                       &mv);
    }

    static void MvInit( TMVB& mv, ScalarType alpha = Teuchos::ScalarTraits<ScalarType>::zero() )
    { Thyra::assign(&mv,alpha); }



    static void MvPrint( const TMVB& mv, std::ostream& os )
      { os << describe(mv,Teuchos::VERB_EXTREME); }


  };        

  //
  // Implementation of the Belos::OperatorTraits for Thyra::LinearOpBase
  //

  template <class ScalarType> 
  class OperatorTraits < ScalarType, Thyra::MultiVectorBase<ScalarType>, Thyra::LinearOpBase<ScalarType> >
  {
  private:
    typedef Thyra::MultiVectorBase<ScalarType> TMVB;
    typedef Thyra::LinearOpBase<ScalarType>    TLOB;
  public:
    
    static void Apply ( const TLOB& Op, const TMVB& x, TMVB& y )
    { 
      Op.apply(Thyra::NONCONJ_ELE,x,&y);
    }
    
  };
  
} // end of Belos namespace 

#endif 
// end of file BELOS_THYRA_ADAPTER_HPP