AbstractLinAlgPack_SparseVectorOpDef.hpp

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// Moocho: Multi-functional Object-Oriented arCHitecture for Optimization
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// The declarations for these functions is in the file AbstractLinAlgPack_SparseVectorOpDecl.hpp
// but because of a bug with the MS VC++ 5.0 compiler you can not use
// namespace qualification with definitions of previously declared
// nonmember template funcitons.  By not including the declarations
// and by including this file for automatic instantiation, then
// if the function prototypes are not the same then a compile
// time error is more likely to occur.  Otherwise you could have
// to settle for a compile-time warning that the funciton has
// not been defined or a link-time error that the definition
// could not be found which will be the case when explicit
// instantiation is used.

// ToDo: 6/9/98 Finish upgrade

#ifndef SPARSE_VECTOR_OP_DEF_H
#define SPARSE_VECTOR_OP_DEF_H

#include "AbstractLinAlgPack_Types.hpp"
#include "AbstractLinAlgPack_SparseVectorClass.hpp"
#include "DenseLinAlgPack_DVectorOp.hpp"
#include "DenseLinAlgPack_DMatrixAsTriSym.hpp"  // also included in AbstractLinAlgPack_SparseVectorOpDef.hpp
#include "DenseLinAlgPack_DMatrixClass.hpp"
#include "DenseLinAlgPack_AssertOp.hpp"

namespace {
template< class T >
inline
T my_my_max( const T& v1, const T& v2 ) { return v1 > v2 ? v1 : v2; }
template< class T >
inline
T my_my_min( const T& v1, const T& v2 ) { return v1 < v2 ? v1 : v2; }
} // end namespace

namespace AbstractLinAlgPack {

using DenseLinAlgPack::VopV_assert_sizes;
using DenseLinAlgPack::Vp_V_assert_sizes;
using DenseLinAlgPack::Vp_MtV_assert_sizes;

using DenseLinAlgPack::row;
using DenseLinAlgPack::col;

namespace SparseVectorUtilityPack {
template<class T_SpVec>
value_type imp_dot2_V_V_SV(const DVectorSlice& vs1, const DVectorSlice& vs2, const T_SpVec& sv);
}

// result = dot(vs_rhs1,sv_rhs2)
template<class T_SpVec>
value_type dot_V_SV(const DVectorSlice& vs_rhs1, const T_SpVec& sv_rhs2) {
  VopV_assert_sizes(vs_rhs1.dim(),sv_rhs2.dim());
  value_type result = 0.0;
  typename T_SpVec::difference_type offset = sv_rhs2.offset();
  for(typename T_SpVec::const_iterator iter = sv_rhs2.begin(); iter != sv_rhs2.end(); ++iter)
    result += vs_rhs1(iter->index()+offset) * iter->value();
  return result;
}

// result = dot(sv_rhs1,vs_rhs2).  Just call the above in reverse order
template<class T_SpVec>
value_type dot_SV_V(const T_SpVec& sv_rhs1, const DVectorSlice& vs_rhs2) {
  return dot_V_SV(vs_rhs2,sv_rhs1);
}

// result = ||sv_rhs||1
template<class T_SpVec>
value_type norm_1_SV(const T_SpVec& sv_rhs) {
  typename T_SpVec::element_type::value_type result = 0.0;
  for(typename T_SpVec::const_iterator iter = sv_rhs.begin(); iter != sv_rhs.end(); ++iter)
    result += ::fabs(iter->value());
  return result;
}

// result = ||sv_rhs||2
template<class T_SpVec>
value_type norm_2_SV(const T_SpVec& sv_rhs) {
  typename T_SpVec::element_type::value_type result = 0.0;
  for(typename T_SpVec::const_iterator iter = sv_rhs.begin(); iter != sv_rhs.end(); ++iter)
    result += (iter->value()) * (iter->value());
  return result;
}

// result = ||sv_rhs||inf
template<class T_SpVec>
value_type norm_inf_SV(const T_SpVec& sv_rhs) {
  typename T_SpVec::element_type::value_type result = 0.0;
  for(typename T_SpVec::const_iterator iter = sv_rhs.begin(); iter != sv_rhs.end(); ++iter)
    result = my_my_max(result,std::fabs(iter->value()));
  return result;
}

// result = max(sv_rhs)
template<class T_SpVec>
value_type max_SV(const T_SpVec& sv_rhs) {
  typename T_SpVec::element_type::value_type result = 0.0;
  for(typename T_SpVec::const_iterator iter = sv_rhs.begin(); iter != sv_rhs.end(); ++iter)
    result = my_my_max(iter->value(),result);
  return result;
}

// result = min(sv_rhs)
template<class T_SpVec>
value_type min_SV(const T_SpVec& sv_rhs) {
  typename T_SpVec::element_type::value_type result = 0.0;
  for(typename T_SpVec::const_iterator iter = sv_rhs.begin(); iter != sv_rhs.end(); ++iter)
    result = my_my_min(result,iter->value());
  return result;
}

// vs_lhs += alpha * sv_rhs (BLAS xAXPY)
template<class T_SpVec>
void Vt_S( T_SpVec* sv_lhs, value_type alpha )
{
  if( alpha == 1.0 ) return;
  for(typename T_SpVec::iterator iter = sv_lhs->begin(); iter != sv_lhs->end(); ++iter)
    iter->value() *= alpha;
}

// vs_lhs += alpha * sv_rhs (BLAS xAXPY)
template<class T_SpVec>
void Vp_StSV(DVectorSlice* vs_lhs, value_type alpha, const T_SpVec& sv_rhs)
{
  Vp_V_assert_sizes(vs_lhs->dim(),sv_rhs.dim());
  typename T_SpVec::difference_type offset = sv_rhs.offset();
  for(typename T_SpVec::const_iterator iter = sv_rhs.begin(); iter != sv_rhs.end(); ++iter)
    (*vs_lhs)(iter->index() + offset) += alpha * iter->value();
}

// vs_lhs += alpha * op(gms_rhs1) * sv_rhs2 (BLAS xGEMV) (time = O(sv_rhs2.nz() * vs_lhs.dim())
template<class T_SpVec>
void Vp_StMtSV(DVectorSlice* pvs_lhs, value_type alpha, const DMatrixSlice& gms_rhs1
  , BLAS_Cpp::Transp trans_rhs1, const T_SpVec& sv_rhs2)
{
#ifdef _WINDOWS
  using DenseLinAlgPack::Vp_StV;  // MS VC++ 6.0 needs help with the name lookups
#endif
  DVectorSlice& vs_lhs = *pvs_lhs;

  Vp_MtV_assert_sizes(vs_lhs.dim(),gms_rhs1.rows(),gms_rhs1.cols(),trans_rhs1
            , sv_rhs2.dim());
  
  // Perform the operation by iterating through the sparse vector and performing
  // all of the operations on it.
  //
  // For sparse element e we do the following:
  //
  // vs_lhs += alpha * e.value() * gms_rhs1.col(e.index());

  typename T_SpVec::difference_type offset = sv_rhs2.offset();

  for(typename T_SpVec::const_iterator sv_rhs2_itr = sv_rhs2.begin(); sv_rhs2_itr != sv_rhs2.end(); ++sv_rhs2_itr)
    DenseLinAlgPack::Vp_StV( &vs_lhs, alpha * sv_rhs2_itr->value()
            , col( gms_rhs1, trans_rhs1, sv_rhs2_itr->index() + offset ) );
}

// vs_lhs += alpha * op(tri_rhs1) * sv_rhs2 (BLAS xTRMV)
template<class T_SpVec>
void Vp_StMtSV(DVectorSlice* pvs_lhs, value_type alpha, const DMatrixSliceTri& tri_rhs1
  , BLAS_Cpp::Transp trans_rhs1, const T_SpVec& sv_rhs2)
{
  DVectorSlice &vs_lhs = *pvs_lhs;

  Vp_MtV_assert_sizes(vs_lhs.dim(),tri_rhs1.rows(),tri_rhs1.cols(),trans_rhs1
            , sv_rhs2.dim());

  // Get the effective matrix
  BLAS_Cpp::Uplo effective_uplo;
  if( (tri_rhs1.uplo() == BLAS_Cpp::lower && trans_rhs1 == BLAS_Cpp::no_trans) ||
    (tri_rhs1.uplo() == BLAS_Cpp::upper && trans_rhs1 == BLAS_Cpp::trans)      )
  {
    effective_uplo = BLAS_Cpp::lower;
  }
  else {  // must be effective upper
    effective_uplo = BLAS_Cpp::upper;
  }

  size_type n = tri_rhs1.gms().rows();  // should be same as cols()

  // Implement the operation by looping through the sparse vector only once
  // and performing the row operations.  This gives a time = O(n * sv_rhs2.nz())
  typename T_SpVec::difference_type offset = sv_rhs2.offset();
  for(typename T_SpVec::const_iterator sv_itr = sv_rhs2.begin(); sv_itr != sv_rhs2.end(); ++sv_itr)
  {
    size_type j = sv_itr->index() + offset;

    // For the nonzero element j = sv_itr->index() we perfom the following
    // operations.
    //
    // Lower:
    //    [\]   [\ 0 0 0] [\]
    //    [#]  += [\ # 0 0] * [#] jth element
    //    [#]   [\ # \ 0] [\]
    //    [#]   [\ # \ \] [\]
    //          jth
    //          col
    //
    // Upper:
    //    [#]   [\ # \ \] [\]
    //    [#]  += [0 # \ \] * [#] jth element
    //    [\]   [0 0 \ \] [\]
    //    [\]   [0 0 0 \] [\]
    //          jth
    //          col
    //
    // If we were told that is it is unit diagonal then we will adjust
    // accordingly.

    size_type j_adjusted = j; // will be adjusted for unit diagonal
    
    switch(effective_uplo) {
      case BLAS_Cpp::lower: {
        if(tri_rhs1.diag() == BLAS_Cpp::unit)
        {
          // Make the adjustment for unit diaganal
          ++j_adjusted;
          vs_lhs(j) += alpha * sv_itr->value(); // diagonal element is one
        }
        // vs_lhs(j,n) = vs_lhs(j,n) + alpha * sv_itr->value() * tri_rhs1.col(j)(j,n)
        if(j_adjusted <= n)
        {
          DenseLinAlgPack::Vp_StV( &vs_lhs(j_adjusted,n), alpha * sv_itr->value()
            ,col(tri_rhs1.gms(),trans_rhs1,j)(j_adjusted,n) );
        }
        break;
      }
      case BLAS_Cpp::upper: {
        if(tri_rhs1.diag() == BLAS_Cpp::unit)
        {
          // Make the adjustment for unit diaganal
          --j_adjusted;
          vs_lhs(j) += alpha * sv_itr->value(); // diagonal element is one
        }
        // vs_lhs(1,j) = vs_lhs(1,j) + alpha * sv_itr->value() * tri_rhs1.col(j)(1,j)
        if(j_adjusted > 0)
        {
          DenseLinAlgPack::Vp_StV( &vs_lhs(1,j_adjusted), alpha * sv_itr->value()
            ,col(tri_rhs1.gms(),trans_rhs1,j)(1,j_adjusted) );
        }
        break;
      }
    }
  }
}

// vs_lhs += alpha * op(sym_rhs1) * sv_rhs2 (BLAS xSYMV)
template<class T_SpVec>
void Vp_StMtSV(DVectorSlice* pvs_lhs, value_type alpha, const DMatrixSliceSym& sym_rhs1
  , BLAS_Cpp::Transp trans_rhs1, const T_SpVec& sv_rhs2)
{
  DVectorSlice& vs_lhs = *pvs_lhs;

  Vp_MtV_assert_sizes(vs_lhs.dim(),sym_rhs1.rows(),sym_rhs1.cols(),trans_rhs1
            , sv_rhs2.dim());

  size_type size = sv_rhs2.dim();
  switch(sym_rhs1.uplo()) {
    case BLAS_Cpp::lower: {
      DVectorSlice::iterator vs_lhs_itr; size_type i;
      for(vs_lhs_itr = vs_lhs.begin(), i = 1; i <= size; ++i)
      {
        if(i < size) {
          *vs_lhs_itr++ +=
            alpha *
            SparseVectorUtilityPack::imp_dot2_V_V_SV(
                     sym_rhs1.gms().row(i)(1,i)
                    ,sym_rhs1.gms().col(i)(i+1,size)
                    ,sv_rhs2);
        }
        else
          *vs_lhs_itr++ += alpha *
            dot_V_SV(sym_rhs1.gms().row(i),sv_rhs2);
      }
      break;      
    }
    case BLAS_Cpp::upper: {
      DVectorSlice::iterator vs_lhs_itr; size_type i;
      for(vs_lhs_itr = vs_lhs.begin(), i = 1; i <= size; ++i)
      {
        if(i > 1) {
          *vs_lhs_itr++ +=
            alpha *
            SparseVectorUtilityPack::imp_dot2_V_V_SV(
                     sym_rhs1.gms().col(i)(1,i-1)
                    ,sym_rhs1.gms().row(i)(i,size)
                    ,sv_rhs2);
        }
        else
        *vs_lhs_itr++ += alpha * dot_V_SV(sym_rhs1.gms().row(i),sv_rhs2);
      }
      break;      
    }
  } 
}

namespace SparseVectorUtilityPack {

// Implementation for the product of a concatonated dense vector with a
// sparse vector.  Used for symetric matrix mulitplication.
// In Matlab notation: result = [vs1' , vs2' ] * sv
// where split = vs1.dim(), vs2.dim() == sv.dim() - split
//
// time = O(sv.nz()), space = O(1)
//
template<class T_SpVec>
value_type imp_dot2_V_V_SV(const DVectorSlice& vs1, const DVectorSlice& vs2, const T_SpVec& sv)
{
  size_type split = vs1.dim();
  value_type result = 0;
  typename T_SpVec::difference_type offset = sv.offset();
  for(typename T_SpVec::const_iterator sv_itr = sv.begin(); sv_itr != sv.end(); ++sv_itr) {
    typename T_SpVec::element_type::indice_type curr_indice = sv_itr->index()+offset;
    if(curr_indice <= split)
      result += vs1(curr_indice) * sv_itr->value();
    else
      result += vs2(curr_indice - split) * sv_itr->value();
  }
  return result;
}

} // end namespace SparseVectorUtilityPack

} // end namespace AbstractLinAlgPack

#endif // SPARSE_VECTOR_OP_DEF_H