特征稀疏矩阵的性能调整

如果X的稀疏性是W的稀疏性的子集,那么您可以编写自己的函数进行就地加法:

namespace Eigen {
template<typename Dst, typename Src>
void inplace_sparse_add(Dst &dst, const Src &src)
{
  EIGEN_STATIC_ASSERT( ((internal::evaluator<Dst>::Flags&RowMajorBit) == (internal::evaluator<Src>::Flags&RowMajorBit)),
                      THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH);

  using internal::evaluator;
  evaluator<Dst> dst_eval(dst);
  evaluator<Src> src_eval(src);

  assert(dst.rows()==src.rows() && dst.cols()==src.cols());
  for (Index j=0; j<src.outerSize(); ++j)
  {
    typename evaluator<Dst>::InnerIterator dst_it(dst_eval, j);
    typename evaluator<Src>::InnerIterator src_it(src_eval, j);
    while(src_it)
    {
      while(dst_it && dst_it.index()!=src_it.index())
        ++dst_it;
      assert(dst_it);
      dst_it.valueRef() += src_it.value();
      ++src_it;
    }
  }
}
}

下面是一个使用示例:

int main()
{
  int n = 10;
  MatrixXd R = MatrixXd::Random(n,n);
  SparseMatrix<double, RowMajor> A = R.sparseView(0.25,1), B = 0.5*R.sparseView(0.65,1);

  cout << A.toDense() << "\n\n" << B.toDense() << "\n\n";

  inplace_sparse_add(A, B);

  cout << A.toDense() << "\n\n";

  auto Ai = A.row(2);
  inplace_sparse_add(Ai, B.row(2));

  cout << A.toDense() << "\n\n";
}