Subsetting Eigen vectors and matrices with a vector of indices

I'm trying to port a working Armadillo function to Eigen and am having an issue with RcppEigen vector and matrix subsetting.

Here's my function:

//[[Rcpp::depends(RcppEigen)]]
#include <RcppEigen.h>
using namespace Eigen;

// [[Rcpp::export]]
Eigen::VectorXd fastnnls_eigen(const Eigen::MatrixXd& a, const Eigen::VectorXd& b, int maxit = 50) {
  Eigen::VectorXd x = (a).llt().solve((b));
  
  while(maxit-- > 0){

    // find values in x greater than zero
    // set values less than zero to zero
    bool x_is_nonneg = true;
    std::vector<int> nz;
    for(int i = 0; i < x.size(); ++i){
      if(x(i) > 0){
        nz.push_back(i);
      }
      else if(x(i) < 0) {
        x_is_nonneg = false; 
        x(i) = 0;
      }
    }
    if(x_is_nonneg) break;

    // update x with solutions from only indices given in "nz"
    x(nz) = a(nz, nz).llt().solve((b(nz)));   // *************ERROR ON THIS LINE
  }
  return(x);
}

It's throwing three errors all on the line indicated above:

no match for call to '(Eigen::VectorXd {aka Eigen::Matrix<double, -1, 1>}) (std::vector<int, std::allocator<int> >&)'
no match for call to '(Eigen::MatrixXd {aka Eigen::Matrix<double, -1, 1>}) (std::vector<int, std::allocator<int> >&)'
no match for call to '(Eigen::VectorXd {aka Eigen::Matrix<double, -1, 1>}) (std::vector<int, std::allocator<int> >&)'

Here's my RcppArmadillo equivalent (working):

//[[Rcpp::export]]
arma::vec fastnnls(const arma::mat& a, const arma::vec& b) {
  arma::vec x = arma::solve(a, b, arma::solve_opts::likely_sympd + arma::solve_opts::fast);
  while (arma::any(x < 0)) {
    arma::uvec nz = arma::find(x > 0);
    x.fill(0);
    x.elem(nz) = arma::solve(a.submat(nz, nz), b.elem(nz), arma::solve_opts::likely_sympd + arma::solve_opts::fast);
  }
  return(x);
}

I'm unsure why Eigen cannot subset using these indices. My implementation seems to be consistent with the Eigen Sub-Matrices documentation.

Any idea why this is throwing an error?

p.s. I've been able to use this same function with RcppArmadillo using the .submat and .elem functions with a uvec indices vector generated by arma::find. There is apparently no equivalent to arma::find in Eigen.

UPDATE I've found documentation directly on this, and I think we can expect support for non-contiguous subviews of Eigen matrices in the (near) future:

https://gitlab.com/libeigen/eigen/-/issues/329

http://eigen.tuxfamily.org/dox-devel/TopicCustomizing_NullaryExpr.html#title1

Apparently this is a much-requested feature in Eigen and is coming in 4.0 (yay!)

As Dirk pointed out, there is no way to do this without copying data to a new matrix, but my microbenchmarking suggests the below Eigen functions do so about 20% faster than Armadillo .submat() and .subvec().

Here is the function for subsetting an object of class Eigen::MatrixBase:

http://eigen.tuxfamily.org/dox-devel/TopicCustomizing_NullaryExpr.html#title1

template<class ArgType, class RowIndexType, class ColIndexType>
class indexing_functor {
  const ArgType& m_arg;
  const RowIndexType& m_rowIndices;
  const ColIndexType& m_colIndices;
public:
  typedef Matrix<typename ArgType::Scalar,
    RowIndexType::SizeAtCompileTime,
    ColIndexType::SizeAtCompileTime,
    ArgType::Flags& RowMajorBit ? RowMajor : ColMajor,
    RowIndexType::MaxSizeAtCompileTime,
    ColIndexType::MaxSizeAtCompileTime> MatrixType;

  indexing_functor(const ArgType& arg, const RowIndexType& row_indices, const ColIndexType& col_indices)
    : m_arg(arg), m_rowIndices(row_indices), m_colIndices(col_indices) {}

  const typename ArgType::Scalar& operator() (Index row, Index col) const {
    return m_arg(m_rowIndices[row], m_colIndices[col]);
  }
};

template <class ArgType, class RowIndexType, class ColIndexType>
CwiseNullaryOp<indexing_functor<ArgType, RowIndexType, ColIndexType>, typename indexing_functor<ArgType, RowIndexType, ColIndexType>::MatrixType>
submat(const Eigen::MatrixBase<ArgType>& arg, const RowIndexType& row_indices, const ColIndexType& col_indices) {
  typedef indexing_functor<ArgType, RowIndexType, ColIndexType> Func;
  typedef typename Func::MatrixType MatrixType;
  return MatrixType::NullaryExpr(row_indices.size(), col_indices.size(), Func(arg.derived(), row_indices, col_indices));
}

And here is how I used that function in my nnls function. This implementation also shows how to subset a vector:

template<typename T, typename Derived>
Eigen::Matrix<T, -1, 1> nnls(const Eigen::Matrix<T, -1, -1>& a, const typename Eigen::MatrixBase<Derived>& b) {
  
  // initialize with unconstrained least squares solution
  Eigen::Matrix<T, -1, 1> x = a.llt().solve(b);
  while ((x.array() < 0).any()) {
    // get indices in "x" greater than zero (the "feasible set")
    Eigen::VectorXi gtz_ind = find_gtz(x);

    // subset "a" and "b" to those indices in the feasible set
    Eigen::Matrix<T, -1, 1> bsub(gtz_ind.size());
    for (unsigned int i = 0; i < gtz_ind.size(); ++i) bsub(i) = b(gtz_ind(i));
    Eigen::Matrix<T, -1, -1> asub = submat(a, gtz_ind, gtz_ind);

    // solve for those indices in "x"
    Eigen::Matrix<T, -1, 1> xsub = asub.llt().solve(bsub);
    x.setZero();
    for (unsigned int i = 0; i < nz.size(); ++i) x(nz(i)) = xsub(i);
  }
  return x;
}