Coarsening.h

// -*- tab-width: 2; indent-tabs-mode: nil; coding: utf-8-with-signature -*-
//-----------------------------------------------------------------------------
// Copyright 2000-2026 CEA (www.cea.fr) IFPEN (www.ifpenergiesnouvelles.com)
// See the top-level COPYRIGHT file for details.
// SPDX-License-Identifier: Apache-2.0
//-----------------------------------------------------------------------------
/*---------------------------------------------------------------------------*/
/* Coarsening.h                                                (C) 2000-2026 */
/*                                                                           */
/* Coarsening strategies for AMG hierarchy construction.                     */
/*---------------------------------------------------------------------------*/
#ifndef ARCCORE_ALINA_COARSENING_H
#define ARCCORE_ALINA_COARSENING_H
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
/*
 * This file is based on the work on AMGCL library (version march 2026)
 * which can be found at https://github.com/ddemidov/amgcl.
 *
 * Copyright (c) 2012-2022 Denis Demidov <dennis.demidov@gmail.com>
 * SPDX-License-Identifier: MIT
 */
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
#include <tuple>
#include <memory>
#include <vector>
#include <numeric>
#include <limits>
#include <algorithm>
#include <cmath>
 
#include "arccore/alina/BuiltinBackend.h"
#include "arccore/alina/AlinaUtils.h"
#include "arccore/alina/QRFactorizationImpl.h"
#include "arccore/alina/Adapters.h"
#include "arccore/alina/ValueTypeInterface.h"
 
#include <mutex>
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
namespace Arcane::Alina::detail
{
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/

template <class Matrix>
std::shared_ptr<Matrix> galerkin(const Matrix& A, const Matrix& P, const Matrix& R)
{
  return product(R, *product(A, P));
}
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
template <class Matrix>
std::shared_ptr<Matrix> scaled_galerkin(const Matrix& A, const Matrix& P, const Matrix& R, float s)
{
  auto a = galerkin(A, P, R);
  scale(*a, s);
  return a;
}
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
} // namespace Arcane::Alina::detail
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
namespace Arcane::Alina
{
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
struct nullspace_params
{
  int cols;


  UniqueArray<double> B;
 
  nullspace_params()
  : cols(0)
  {}
 
  nullspace_params(const PropertyTree& p)
  : cols(p.get("cols", nullspace_params().cols))
  {
    double* b = 0;
    b = p.get("B", b);
 
    if (b) {
      Int32 rows = 0;
      rows = p.get("rows", rows);
 
      precondition(cols > 0, "Error in nullspace parameters: "
                             "B is set, but cols is not");
 
      precondition(rows > 0, "Error in nullspace parameters: "
                             "B is set, but rows is not");
 
      B.assign(b, b + rows * cols);
    }
    else {
      precondition(cols == 0, "Error in nullspace parameters: "
                              "cols > 0, but B is empty");
    }
 
    p.check_params( { "cols", "rows", "B" });
  }
 
  void get(PropertyTree&, const std::string&) const {}
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
struct plain_aggregates
{
  struct params
  {

    float eps_strong;
 
    params()
    : eps_strong(0.08f)
    {}
 
    params(const PropertyTree& p)
    : ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, eps_strong)
    {
      p.check_params( { "eps_strong", "block_size" });
    }
 
    void get(PropertyTree& p, const std::string& path) const
    {
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, eps_strong);
    }
  };
 
  static const ptrdiff_t undefined = -1;
  static const ptrdiff_t removed = -2;

  size_t count;

  UniqueArray<char> strong_connection;

  UniqueArray<ptrdiff_t> id;

  template <class Matrix>
  plain_aggregates(const Matrix& A, const params& prm)
  : count(0)
  , strong_connection(backend::nonzeros(A))
  , id(backend::nbRow(A))
  {
    typedef typename backend::value_type<Matrix>::type value_type;
    typedef typename math::scalar_of<value_type>::type scalar_type;
 
    scalar_type eps_squared = prm.eps_strong * prm.eps_strong;
 
    const size_t n = backend::nbRow(A);
 
    /* 1. Get strong connections */
    auto dia = diagonal(A);
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        value_type eps_dia_i = eps_squared * (*dia)[i];
 
        for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j) {
          ptrdiff_t c = A.col[j];
          value_type v = A.val[j];
 
          strong_connection[j] = (c != i) && (eps_dia_i * (*dia)[c] < v * v);
        }
      }
    });
 
    /* 2. Get aggregate ids */
 
    // Remove lonely nodes.
    size_t max_neib = 0;
    for (size_t i = 0; i < n; ++i) {
      ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1];
      max_neib = std::max<size_t>(max_neib, e - j);
 
      ptrdiff_t state = removed;
      for (; j < e; ++j)
        if (strong_connection[j]) {
          state = undefined;
          break;
        }
 
      id[i] = state;
    }
 
    UniqueArray<ptrdiff_t> neib;
    neib.reserve(max_neib);
 
    // Perform plain aggregation
    for (size_t i = 0; i < n; ++i) {
      if (id[i] != undefined)
        continue;
 
      // The point is not adjacent to a core of any previous aggregate:
      // so its a seed of a new aggregate.
      ptrdiff_t cur_id = static_cast<ptrdiff_t>(count++);
      id[i] = cur_id;
 
      // (*) Include its neighbors as well.
      neib.clear();
      for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j) {
        ptrdiff_t c = A.col[j];
        if (strong_connection[j] && id[c] != removed) {
          id[c] = cur_id;
          neib.push_back(c);
        }
      }
 
      // Temporarily mark undefined points adjacent to the new aggregate
      // as members of the aggregate.
      // If nobody claims them later, they will stay here.
      for (ptrdiff_t c : neib) {
        for (ptrdiff_t j = A.ptr[c], e = A.ptr[c + 1]; j < e; ++j) {
          ptrdiff_t cc = A.col[j];
          if (strong_connection[j] && id[cc] == undefined)
            id[cc] = cur_id;
        }
      }
    }
 
    if (!count)
      throw error::empty_level();
 
    // Some of the aggregates could potentially vanish during expansion
    // step (*) above. We need to exclude those and renumber the rest.
    UniqueArray<ptrdiff_t> cnt(count, 0);
    for (ptrdiff_t i : id)
      if (i >= 0)
        cnt[i] = 1;
    std::partial_sum(cnt.begin(), cnt.end(), cnt.begin());
 
    if (static_cast<ptrdiff_t>(count) > cnt.back()) {
      count = cnt.back();
 
      for (size_t i = 0; i < n; ++i)
        if (id[i] >= 0)
          id[i] = cnt[id[i]] - 1;
    }
  }
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
namespace detail
{
  struct skip_negative
  {
    const UniqueArray<ptrdiff_t>& key;
    int block_size;
 
    skip_negative(const UniqueArray<ptrdiff_t>& key, int block_size)
    : key(key)
    , block_size(block_size)
    {}
 
    bool operator()(ptrdiff_t i, ptrdiff_t j) const
    {
      // Cast to unsigned type to keep negative values at the end
      return static_cast<size_t>(key[i]) / block_size <
      static_cast<size_t>(key[j]) / block_size;
    }
  };
} // namespace detail
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
template <class Matrix>
std::shared_ptr<Matrix>
tentative_prolongation(size_t n,
                       size_t naggr,
                       const UniqueArray<ptrdiff_t> aggr,
                       nullspace_params& nullspace,
                       int block_size)
{
  typedef typename backend::value_type<Matrix>::type value_type;
  typedef typename backend::col_type<Matrix>::type col_type;
 
  auto P = std::make_shared<Matrix>();
 
  ARCCORE_ALINA_TIC("tentative");
  if (nullspace.cols > 0) {
    ptrdiff_t nba = naggr / block_size;
 
    // Sort fine points by aggregate number.
    // Put points not belonging to any aggregate to the end of the list.
    UniqueArray<ptrdiff_t> order(n);
    for (size_t i = 0; i < n; ++i)
      order[i] = i;
    std::stable_sort(order.begin(), order.end(), detail::skip_negative(aggr, block_size));
    UniqueArray<ptrdiff_t> aggr_ptr(nba + 1, 0);
    for (ptrdiff_t i = 0; i < static_cast<ptrdiff_t>(n); ++i) {
      ptrdiff_t a = aggr[order[i]];
      if (a < 0)
        break;
      ++aggr_ptr[a / block_size + 1];
    }
    std::partial_sum(aggr_ptr.begin(), aggr_ptr.end(), aggr_ptr.begin());
 
    // Precompute the shape of the prolongation operator.
    // Each row contains exactly nullspace.cols non-zero entries.
    // Rows that do not belong to any aggregate are empty.
    P->set_size(n, nullspace.cols * nba);
    P->ptr[0] = 0;
 
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        P->ptr[i + 1] = aggr[i] < 0 ? 0 : nullspace.cols;
      }
    });
 
    P->scan_row_sizes();
    P->set_nonzeros();
 
    // Compute the tentative prolongation operator and null-space vectors
    // for the coarser level.
    UniqueArray<double> Bnew;
    Bnew.resize(nba * nullspace.cols * nullspace.cols);
 
    arccoreParallelFor(0, nba, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      Alina::detail::QRFactorization<double> qr;
      UniqueArray<double> Bpart;
 
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        auto aggr_beg = aggr_ptr[i];
        auto aggr_end = aggr_ptr[i + 1];
        auto d = aggr_end - aggr_beg;
 
        Bpart.resize(d * nullspace.cols);
 
        for (ptrdiff_t j = aggr_beg, jj = 0; j < aggr_end; ++j, ++jj) {
          ptrdiff_t ib = nullspace.cols * order[j];
          for (int k = 0; k < nullspace.cols; ++k)
            Bpart[jj + d * k] = nullspace.B[ib + k];
        }
 
        qr.factorize(d, nullspace.cols, &Bpart[0], Alina::detail::col_major);
 
        for (int ii = 0, kk = 0; ii < nullspace.cols; ++ii)
          for (int jj = 0; jj < nullspace.cols; ++jj, ++kk)
            Bnew[i * nullspace.cols * nullspace.cols + kk] = qr.R(ii, jj);
 
        for (ptrdiff_t j = aggr_beg, ii = 0; j < aggr_end; ++j, ++ii) {
          col_type* c = &P->col[P->ptr[order[j]]];
          value_type* v = &P->val[P->ptr[order[j]]];
 
          for (int jj = 0; jj < nullspace.cols; ++jj) {
            c[jj] = i * nullspace.cols + jj;
            // TODO: this is just a workaround to make non-scalar value
            // types compile. Most probably this won't actually work.
            v[jj] = qr.Q(ii, jj) * math::identity<value_type>();
          }
        }
      }
    });
 
    std::swap(nullspace.B, Bnew);
  }
  else {
    P->set_size(n, naggr);
    P->ptr[0] = 0;
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        P->ptr[i + 1] = (aggr[i] >= 0);
      }
    });
 
    P->set_nonzeros(P->scan_row_sizes());
 
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        if (aggr[i] >= 0) {
          P->col[P->ptr[i]] = aggr[i];
          P->val[P->ptr[i]] = math::identity<value_type>();
        }
      }
    });
  }
  ARCCORE_ALINA_TOC("tentative");
 
  return P;
}
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
class pointwise_aggregates
{
 public:

  struct params : plain_aggregates::params
  {
    Int32 block_size = 1;
 
    params() = default;
 
    params(const PropertyTree& p)
    : plain_aggregates::params(p)
    , ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, block_size)
    {
      p.check_params( { "eps_strong", "block_size" });
    }
 
    void get(Alina::PropertyTree& p, const std::string& path) const
    {
      plain_aggregates::params::get(p, path);
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, block_size);
    }
  };
 
  static const ptrdiff_t undefined = -1;
  static const ptrdiff_t removed = -2;

  size_t count;

  UniqueArray<char> strong_connection;

  UniqueArray<ptrdiff_t> id;

  template <class Matrix>
  pointwise_aggregates(const Matrix& A, const params& prm, unsigned min_aggregate)
  : count(0)
  {
    if (prm.block_size == 1) {
      plain_aggregates aggr(A, prm);
 
      remove_small_aggregates(A.nbRow(), 1, min_aggregate, aggr);
 
      count = aggr.count;
      strong_connection.swap(aggr.strong_connection);
      id.swap(aggr.id);
    }
    else {
      strong_connection.resize(backend::nonzeros(A));
      id.resize(backend::nbRow(A));
 
      auto ap = pointwise_matrix(A, prm.block_size);
      auto& Ap = *ap;
 
      plain_aggregates pw_aggr(Ap, prm);
 
      remove_small_aggregates(Ap.nbRow(), prm.block_size, min_aggregate, pw_aggr);
 
      count = pw_aggr.count * prm.block_size;
 
      arccoreParallelFor(0, Ap.nbRow(), ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
        UniqueArray<ptrdiff_t> j(prm.block_size);
        UniqueArray<ptrdiff_t> e(prm.block_size);
 
        for (ptrdiff_t ip = begin; ip < (begin + size); ++ip) {
          ptrdiff_t ia = ip * prm.block_size;
 
          for (unsigned k = 0; k < prm.block_size; ++k, ++ia) {
            id[ia] = prm.block_size * pw_aggr.id[ip] + k;
 
            j[k] = A.ptr[ia];
            e[k] = A.ptr[ia + 1];
          }
 
          for (ptrdiff_t jp = Ap.ptr[ip], ep = Ap.ptr[ip + 1]; jp < ep; ++jp) {
            ptrdiff_t cp = Ap.col[jp];
            bool sp = (cp == ip) || pw_aggr.strong_connection[jp];
 
            ptrdiff_t col_end = (cp + 1) * prm.block_size;
 
            for (unsigned k = 0; k < prm.block_size; ++k) {
              ptrdiff_t beg = j[k];
              ptrdiff_t end = e[k];
 
              while (beg < end && A.col[beg] < col_end) {
                strong_connection[beg] = sp && A.col[beg] != (ia + k);
                ++beg;
              }
 
              j[k] = beg;
            }
          }
        }
      });
    }
  }
 
  static void remove_small_aggregates(size_t n, unsigned block_size, unsigned min_aggregate,
                                      plain_aggregates& aggr)
  {
    if (min_aggregate <= 1)
      return; // nothing to do
 
    // Count entries in each of the aggregates
    UniqueArray<ptrdiff_t> count(aggr.count, 0);
 
    for (size_t i = 0; i < n; ++i) {
      ptrdiff_t id = aggr.id[i];
      if (id != removed)
        ++count[id];
    }
 
    // If any aggregate has less entries than required, remove it.
    // Renumber the rest of the aggregates to leave no gaps.
    size_t m = 0;
    for (size_t i = 0; i < aggr.count; ++i) {
      if (block_size * count[i] < min_aggregate) {
        count[i] = removed;
      }
      else {
        count[i] = m++;
      }
    }
 
    // Update aggregate count and aggregate ids.
    aggr.count = m;
 
    for (size_t i = 0; i < n; ++i) {
      ptrdiff_t id = aggr.id[i];
      if (id != removed)
        aggr.id[i] = count[id];
    }
  }
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
template <class Backend>
struct AggregationCoarsening
{
  typedef pointwise_aggregates Aggregates;

  struct params
  {
    Aggregates::params aggr;

    nullspace_params nullspace;

    float over_interp;
 
    params()
    : over_interp(math::static_rows<typename Backend::value_type>::value == 1 ? 1.5f : 2.0f)
    {}
 
    params(const PropertyTree& p)
    : ARCCORE_ALINA_PARAMS_IMPORT_CHILD(p, aggr)
    , ARCCORE_ALINA_PARAMS_IMPORT_CHILD(p, nullspace)
    , ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, over_interp)
    {
      p.check_params( { "aggr", "nullspace", "over_interp" });
    }
 
    void get(PropertyTree& p, const std::string& path) const
    {
      ARCCORE_ALINA_PARAMS_EXPORT_CHILD(p, path, aggr);
      ARCCORE_ALINA_PARAMS_EXPORT_CHILD(p, path, nullspace);
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, over_interp);
    }
  } prm;
 
  explicit AggregationCoarsening(const params& prm = params())
  : prm(prm)
  {}

  template <class Matrix>
  std::tuple<std::shared_ptr<Matrix>, std::shared_ptr<Matrix>>
  transfer_operators(const Matrix& A)
  {
    const size_t n = backend::nbRow(A);
 
    ARCCORE_ALINA_TIC("aggregates");
    Aggregates aggr(A, prm.aggr, prm.nullspace.cols);
    ARCCORE_ALINA_TOC("aggregates");
 
    ARCCORE_ALINA_TIC("interpolation");
    auto P = tentative_prolongation<Matrix>(
    n, aggr.count, aggr.id, prm.nullspace, prm.aggr.block_size);
    ARCCORE_ALINA_TOC("interpolation");
 
    return std::make_tuple(P, transpose(*P));
  }

  template <class Matrix>
  std::shared_ptr<Matrix>
  coarse_operator(const Matrix& A, const Matrix& P, const Matrix& R) const
  {
    return detail::scaled_galerkin(A, P, R, 1 / prm.over_interp);
  }
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
template <template <class> class Coarsening>
struct AsScalarCoarsening
{
  template <class Backend>
  struct type
  {
    typedef math::element_of<typename Backend::value_type>::type Scalar;
    typedef BuiltinBackend<Scalar, typename Backend::col_type, typename Backend::ptr_type> BaseBackend;
    typedef Coarsening<BaseBackend> Base;
 
    typedef typename Base::params params;
    Base base;
 
    type(const params& prm = params())
    : base(prm) {};
 
    template <class Matrix>
    typename std::enable_if<backend::coarsening_is_supported<BaseBackend, Coarsening>::value &&
                            (math::static_rows<typename backend::value_type<Matrix>::type>::value > 1),
                            std::tuple<std::shared_ptr<Matrix>, std::shared_ptr<Matrix>>>::type
    transfer_operators(const Matrix& B)
    {
      typedef typename backend::value_type<Matrix>::type Block;
      auto T = base.transfer_operators(*adapter::unblock_matrix(B));
 
      auto& P = *std::get<0>(T);
      auto& R = *std::get<1>(T);
 
      sort_rows(P);
      sort_rows(R);
 
      return std::make_tuple(
      std::make_shared<Matrix>(adapter::block_matrix<Block>(P)),
      std::make_shared<Matrix>(adapter::block_matrix<Block>(R)));
    }
 
    template <class Matrix>
    typename std::enable_if<backend::coarsening_is_supported<BaseBackend, Coarsening>::value &&
                            (math::static_rows<typename backend::value_type<Matrix>::type>::value == 1),
                            std::tuple<std::shared_ptr<Matrix>, std::shared_ptr<Matrix>>>::type
    transfer_operators(const Matrix& A)
    {
      return base.transfer_operators(A);
    }
 
    template <class Matrix>
    typename std::enable_if<!backend::coarsening_is_supported<BaseBackend, Coarsening>::value,
                            std::tuple<std::shared_ptr<Matrix>, std::shared_ptr<Matrix>>>::type
    transfer_operators(const Matrix&)
    {
      throw std::logic_error("The coarsening is not supported by the backend");
    }
 
    template <class Matrix>
    std::shared_ptr<Matrix>
    coarse_operator(const Matrix& A, const Matrix& P, const Matrix& R) const
    {
      return base.coarse_operator(A, P, R);
    }
  };
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
// Create rigid body modes from coordinate vector.
// To be used as near-nullspace vectors with aggregation coarsening
// for 2D or 3D elasticity problems.
// The output matrix B may be transposed on demand
// (to be used as a set of deflation vectors).
template <typename Vector1, typename Vector2>
int rigid_body_modes(int ndim, const Vector1& coo, Vector2& B, bool transpose = false)
{
  precondition(ndim == 2 || ndim == 3, "Only 2D or 3D problems are supported");
  precondition(coo.size() % ndim == 0, "Coordinate vector size should be divisible by ndim");
 
  size_t n = coo.size();
  int nmodes = (ndim == 2 ? 3 : 6);
  B.resize(n * nmodes, 0.0);
 
  const int stride1 = transpose ? 1 : nmodes;
  const int stride2 = transpose ? n : 1;
 
  double sn = 1 / sqrt(n);
 
  if (ndim == 2) {
    for (size_t i = 0; i < n; ++i) {
      size_t nod = i / ndim;
      size_t dim = i % ndim;
 
      double x = coo[nod * 2 + 0];
      double y = coo[nod * 2 + 1];
 
      // Translation
      B[i * stride1 + dim * stride2] = sn;
 
      // Rotation
      switch (dim) {
      case 0:
        B[i * stride1 + 2 * stride2] = -y;
        break;
      case 1:
        B[i * stride1 + 2 * stride2] = x;
        break;
      }
    }
  }
  else if (ndim == 3) {
    for (size_t i = 0; i < n; ++i) {
      size_t nod = i / ndim;
      size_t dim = i % ndim;
 
      double x = coo[nod * 3 + 0];
      double y = coo[nod * 3 + 1];
      double z = coo[nod * 3 + 2];
 
      // Translation
      B[i * stride1 + dim * stride2] = sn;
 
      // Rotation
      switch (dim) {
      case 0:
        B[i * stride1 + 3 * stride2] = y;
        B[i * stride1 + 5 * stride2] = z;
        break;
      case 1:
        B[i * stride1 + 3 * stride2] = -x;
        B[i * stride1 + 4 * stride2] = -z;
        break;
      case 2:
        B[i * stride1 + 4 * stride2] = y;
        B[i * stride1 + 5 * stride2] = -x;
        break;
      }
    }
  }
 
  // Orthonormalization
  std::array<double, 6> dot;
  for (int i = ndim; i < nmodes; ++i) {
    std::fill(dot.begin(), dot.end(), 0.0);
    for (size_t j = 0; j < n; ++j) {
      for (int k = 0; k < i; ++k)
        dot[k] += B[j * stride1 + k * stride2] * B[j * stride1 + i * stride2];
    }
    double s = 0.0;
    for (size_t j = 0; j < n; ++j) {
      for (int k = 0; k < i; ++k)
        B[j * stride1 + i * stride2] -= dot[k] * B[j * stride1 + k * stride2];
      s += B[j * stride1 + i * stride2] * B[j * stride1 + i * stride2];
    }
    s = sqrt(s);
    for (size_t j = 0; j < n; ++j)
      B[j * stride1 + i * stride2] /= s;
  }
 
  return nmodes;
}
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
template <class Backend>
struct RugeStubenCoarsening
{
  struct params
  {
    float eps_strong = 0.25f;

    bool do_trunc = true;

    float eps_trunc = 0.2f;
 
    params() = default;
 
    params(const PropertyTree& p)
    : ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, eps_strong)
    , ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, do_trunc)
    , ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, eps_trunc)
    {
      p.check_params( { "eps_strong", "do_trunc", "eps_trunc" });
    }
 
    void get(PropertyTree& p, const std::string& path) const
    {
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, eps_strong);
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, do_trunc);
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, eps_trunc);
    }
  } prm;
 
  explicit RugeStubenCoarsening(const params& prm = params())
  : prm(prm)
  {}
 
  template <class Matrix>
  std::tuple<std::shared_ptr<Matrix>, std::shared_ptr<Matrix>>
  transfer_operators(const Matrix& A) const
  {
    typedef typename backend::value_type<Matrix>::type Val;
    typedef typename backend::col_type<Matrix>::type Col;
    typedef typename backend::ptr_type<Matrix>::type Ptr;
    typedef typename math::scalar_of<Val>::type Scalar;
 
    const size_t n = backend::nbRow(A);
 
    static const Scalar eps = Alina::detail::eps<Scalar>(1);
 
    static const Val zero = math::zero<Val>();
 
    UniqueArray<char> cf(n, 'U');
    CSRMatrix<char, Col, Ptr> S;
 
    ARCCORE_ALINA_TIC("C/F split");
    connect(A, prm.eps_strong, S, cf);
    cfsplit(A, S, cf);
    ARCCORE_ALINA_TOC("C/F split");
 
    ARCCORE_ALINA_TIC("interpolation");
    size_t nc = 0;
    UniqueArray<ptrdiff_t> cidx(n);
    for (size_t i = 0; i < n; ++i)
      if (cf[i] == 'C')
        cidx[i] = static_cast<ptrdiff_t>(nc++);
 
    if (!nc)
      throw error::empty_level();
 
    auto P = std::make_shared<Matrix>();
    P->set_size(n, nc, true);
 
    UniqueArray<Val> Amin, Amax;
 
    if (prm.do_trunc) {
      Amin.resize(n);
      Amax.resize(n);
    }
 
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        if (cf[i] == 'C') {
          ++P->ptr[i + 1];
          continue;
        }
 
        if (prm.do_trunc) {
          Val amin = zero, amax = zero;
 
          for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j) {
            if (!S.val[j] || cf[A.col[j]] != 'C')
              continue;
 
            amin = std::min(amin, A.val[j]);
            amax = std::max(amax, A.val[j]);
          }
 
          Amin[i] = (amin *= prm.eps_trunc);
          Amax[i] = (amax *= prm.eps_trunc);
 
          for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j) {
            if (!S.val[j] || cf[A.col[j]] != 'C')
              continue;
 
            if (A.val[j] < amin || amax < A.val[j])
              ++P->ptr[i + 1];
          }
        }
        else {
          for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j)
            if (S.val[j] && cf[A.col[j]] == 'C')
              ++P->ptr[i + 1];
        }
      }
    });
 
    P->set_nonzeros(P->scan_row_sizes());
 
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        ptrdiff_t row_head = P->ptr[i];
 
        if (cf[i] == 'C') {
          P->col[row_head] = cidx[i];
          P->val[row_head] = math::identity<Val>();
          continue;
        }
 
        Val dia = zero;
        Val a_num = zero, a_den = zero;
        Val b_num = zero, b_den = zero;
        Val d_neg = zero, d_pos = zero;
 
        for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j) {
          ptrdiff_t c = A.col[j];
          Val v = A.val[j];
 
          if (c == i) {
            dia = v;
            continue;
          }
 
          if (v < zero) {
            a_num += v;
            if (S.val[j] && cf[c] == 'C') {
              a_den += v;
              if (prm.do_trunc && Amin[i] < v)
                d_neg += v;
            }
          }
          else {
            b_num += v;
            if (S.val[j] && cf[c] == 'C') {
              b_den += v;
              if (prm.do_trunc && v < Amax[i])
                d_pos += v;
            }
          }
        }
 
        Scalar cf_neg = 1;
        Scalar cf_pos = 1;
 
        if (prm.do_trunc) {
          if (math::norm(static_cast<Val>(a_den - d_neg)) > eps)
            cf_neg = math::norm(a_den) / math::norm(static_cast<Val>(a_den - d_neg));
 
          if (math::norm(static_cast<Val>(b_den - d_pos)) > eps)
            cf_pos = math::norm(b_den) / math::norm(static_cast<Val>(b_den - d_pos));
        }
 
        if (zero < b_num && math::norm(b_den) < eps)
          dia += b_num;
 
        Scalar alpha = math::norm(a_den) > eps ? -cf_neg * math::norm(a_num) / (math::norm(dia) * math::norm(a_den)) : 0;
        Scalar beta = math::norm(b_den) > eps ? -cf_pos * math::norm(b_num) / (math::norm(dia) * math::norm(b_den)) : 0;
 
        for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j) {
          ptrdiff_t c = A.col[j];
          Val v = A.val[j];
 
          if (!S.val[j] || cf[c] != 'C')
            continue;
          if (prm.do_trunc && Amin[i] <= v && v <= Amax[i])
            continue;
 
          P->col[row_head] = cidx[c];
          P->val[row_head] = (v < zero ? alpha : beta) * v;
          ++row_head;
        }
      }
    });
    ARCCORE_ALINA_TOC("interpolation");
 
    return std::make_tuple(P, transpose(*P));
  }
 
  template <class Matrix>
  std::shared_ptr<Matrix>
  coarse_operator(const Matrix& A, const Matrix& P, const Matrix& R) const
  {
    return detail::galerkin(A, P, R);
  }
 
 private:
 
  //-------------------------------------------------------------------
  // On return S will hold both strong connection matrix (in S.val, which
  // is piggybacking A.ptr and A.col), and its transposition (in S.ptr
  // and S.val).
  //
  // Variables that have no positive connections are marked as F(ine).
  //-------------------------------------------------------------------
  template <typename Val, typename Col, typename Ptr>
  static void connect(CSRMatrix<Val, Col, Ptr> const& A, float eps_strong,
                      CSRMatrix<char, Col, Ptr>& S,
                      UniqueArray<char>& cf)
  {
    typedef typename math::scalar_of<Val>::type Scalar;
 
    const size_t n = backend::nbRow(A);
    const size_t nnz = backend::nonzeros(A);
    const Scalar eps = Alina::detail::eps<Scalar>(1);
 
    S.setNbRow(n);
    S.ncols = n;
    S.ptr.resize(n + 1);
    S.val.resize(nnz);
    S.ptr[0] = 0;
 
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
 
        S.ptr[i + 1] = 0;
 
        Val a_min = math::zero<Val>();
 
        for (auto a = backend::row_begin(A, i); a; ++a)
          if (a.col() != i)
            a_min = std::min(a_min, a.value());
 
        if (math::norm(a_min) < eps) {
          cf[i] = 'F';
          continue;
        }
 
        a_min *= eps_strong;
 
        for (Ptr j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j)
          S.val[j] = (A.col[j] != i && A.val[j] < a_min);
      }
    });
 
    // Transposition of S:
    for (size_t i = 0; i < nnz; ++i)
      if (S.val[i])
        ++(S.ptr[A.col[i] + 1]);
 
    S.scan_row_sizes();
    S.col.resize(S.ptr[n]);
 
    for (size_t i = 0; i < n; ++i)
      for (Ptr j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j)
        if (S.val[j])
          S.col[S.ptr[A.col[j]]++] = i;
 
    std::rotate(S.ptr.data(), S.ptr.data() + n, S.ptr.data() + n + 1);
    S.ptr[0] = 0;
  }
 
  // Split variables into C(oarse) and F(ine) sets.
  template <typename Val, typename Col, typename Ptr>
  static void cfsplit(CSRMatrix<Val, Col, Ptr> const& A,
                      CSRMatrix<char, Col, Ptr> const& S,
                      UniqueArray<char>& cf)
  {
    const size_t n = A.nbRow();
 
    UniqueArray<Col> lambda(n);
 
    // Initialize lambdas:
    for (size_t i = 0; i < n; ++i) {
      Col temp = 0;
      for (Ptr j = S.ptr[i], e = S.ptr[i + 1]; j < e; ++j)
        temp += (cf[S.col[j]] == 'U' ? 1 : 2);
      lambda[i] = temp;
    }
 
    // Keep track of variable groups with equal lambda values.
    // ptr - start of a group;
    // cnt - size of a group;
    // i2n - variable number;
    // n2i - variable position in a group.
    UniqueArray<Ptr> ptr(n + 1, 0);
    UniqueArray<Ptr> cnt(n, 0);
    UniqueArray<Ptr> i2n(n);
    UniqueArray<Ptr> n2i(n);
 
    for (size_t i = 0; i < n; ++i)
      ++ptr[lambda[i] + 1];
 
    std::partial_sum(ptr.begin(), ptr.end(), ptr.begin());
 
    for (size_t i = 0; i < n; ++i) {
      Col lam = lambda[i];
      Ptr idx = ptr[lam] + cnt[lam]++;
      i2n[idx] = i;
      n2i[i] = idx;
    }
 
    // Process variables by decreasing lambda value.
    // 1. The variable with maximum value of lambda becomes next C-variable.
    // 2. Its neighbours from S' become F-variables.
    // 3. Keep lambda values in sync.
    for (size_t top = n; top-- > 0;) {
      Ptr i = i2n[top];
      Col lam = lambda[i];
 
      if (lam == 0) {
        std::replace(cf.begin(), cf.end(), 'U', 'C');
        break;
      }
 
      // Remove the variable from its group.
      --cnt[lam];
 
      if (cf[i] == 'F')
        continue;
 
      // Mark the variable as 'C'.
      cf[i] = 'C';
 
      // Its neighbours from S' become F-variables.
      for (Ptr j = S.ptr[i], e = S.ptr[i + 1]; j < e; ++j) {
        Col c = S.col[j];
 
        if (cf[c] != 'U')
          continue;
 
        cf[c] = 'F';
 
        // Increase lambdas of the newly created F's neighbours.
        for (Ptr aj = A.ptr[c], ae = A.ptr[c + 1]; aj < ae; ++aj) {
          if (!S.val[aj])
            continue;
 
          Col ac = A.col[aj];
          Col lam_a = lambda[ac];
 
          if (cf[ac] != 'U' || static_cast<size_t>(lam_a) + 1 >= n)
            continue;
 
          Ptr old_pos = n2i[ac];
          Ptr new_pos = ptr[lam_a] + cnt[lam_a] - 1;
 
          n2i[i2n[old_pos]] = new_pos;
          n2i[i2n[new_pos]] = old_pos;
 
          std::swap(i2n[old_pos], i2n[new_pos]);
 
          --cnt[lam_a];
          ++cnt[lam_a + 1];
          ptr[lam_a + 1] = ptr[lam_a] + cnt[lam_a];
 
          lambda[ac] = lam_a + 1;
        }
      }
 
      // Decrease lambdas of the newly created C's neighbours.
      for (Ptr j = A.ptr[i], e = A.ptr[i + 1]; j < e; j++) {
        if (!S.val[j])
          continue;
 
        Col c = A.col[j];
        Col lam = lambda[c];
 
        if (cf[c] != 'U' || lam == 0)
          continue;
 
        Ptr old_pos = n2i[c];
        Ptr new_pos = ptr[lam];
 
        n2i[i2n[old_pos]] = new_pos;
        n2i[i2n[new_pos]] = old_pos;
 
        std::swap(i2n[old_pos], i2n[new_pos]);
 
        --cnt[lam];
        ++cnt[lam - 1];
        ++ptr[lam];
        lambda[c] = lam - 1;
      }
    }
  }
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
template <class Backend>
struct SmoothedAggregationCoarserning
{
  typedef pointwise_aggregates Aggregates;

  struct params
  {
    Aggregates::params aggr;

    nullspace_params nullspace;

    float relax = 1.0f;
 
    // Estimate the matrix spectral radius.
    // This usually improves convergence rate and results in faster solves,
    // but costs some time during setup.
    bool estimate_spectral_radius = false;
 
    // Number of power iterations to apply for the spectral radius
    // estimation. Use Gershgorin disk theorem when power_iters = 0.
    int power_iters = 0;
 
    params() = default;
 
    params(const PropertyTree& p)
    : ARCCORE_ALINA_PARAMS_IMPORT_CHILD(p, aggr)
    , ARCCORE_ALINA_PARAMS_IMPORT_CHILD(p, nullspace)
    , ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, relax)
    , ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, estimate_spectral_radius)
    , ARCCORE_ALINA_PARAMS_IMPORT_VALUE(p, power_iters)
    {
      p.check_params({ "aggr", "nullspace", "relax", "estimate_spectral_radius", "power_iters" });
    }
 
    void get(PropertyTree& p, const std::string& path) const
    {
      ARCCORE_ALINA_PARAMS_EXPORT_CHILD(p, path, aggr);
      ARCCORE_ALINA_PARAMS_EXPORT_CHILD(p, path, nullspace);
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, relax);
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, estimate_spectral_radius);
      ARCCORE_ALINA_PARAMS_EXPORT_VALUE(p, path, power_iters);
    }
  };
 
  SmoothedAggregationCoarserning(const params& prm = params())
  : prm(prm)
  {}
 
  template <class Matrix>
  std::tuple<std::shared_ptr<Matrix>, std::shared_ptr<Matrix>>
  transfer_operators(const Matrix& A)
  {
    typedef typename backend::value_type<Matrix>::type value_type;
    typedef typename math::scalar_of<value_type>::type scalar_type;
 
    const size_t n = backend::nbRow(A);
 
    ARCCORE_ALINA_TIC("aggregates");
    Aggregates aggr(A, prm.aggr, prm.nullspace.cols);
    prm.aggr.eps_strong *= 0.5;
    ARCCORE_ALINA_TOC("aggregates");
 
    auto P_tent = tentative_prolongation<Matrix>(n, aggr.count, aggr.id, prm.nullspace, prm.aggr.block_size);
 
    auto P = std::make_shared<Matrix>();
    P->set_size(backend::nbRow(*P_tent), backend::nbColumn(*P_tent), true);
 
    scalar_type omega = prm.relax;
    if (prm.estimate_spectral_radius) {
      omega *= static_cast<scalar_type>(4.0 / 3) / spectral_radius<true>(A, prm.power_iters);
    }
    else {
      omega *= static_cast<scalar_type>(2.0 / 3);
    }
 
    ARCCORE_ALINA_TIC("smoothing");
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      UniqueArray<ptrdiff_t> marker(P->ncols, -1);
 
      // Count number of entries in P.
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        for (ptrdiff_t ja = A.ptr[i], ea = A.ptr[i + 1]; ja < ea; ++ja) {
          ptrdiff_t ca = A.col[ja];
 
          // Skip weak off-diagonal connections.
          if (ca != i && !aggr.strong_connection[ja])
            continue;
 
          for (ptrdiff_t jp = P_tent->ptr[ca], ep = P_tent->ptr[ca + 1]; jp < ep; ++jp) {
            ptrdiff_t cp = P_tent->col[jp];
 
            if (marker[cp] != i) {
              marker[cp] = i;
              ++(P->ptr[i + 1]);
            }
          }
        }
      }
    });
 
    P->scan_row_sizes();
    P->set_nonzeros();
 
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      UniqueArray<ptrdiff_t> marker(P->ncols, -1);
 
      // Fill the interpolation matrix.
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
 
        // Diagonal of the filtered matrix is the original matrix
        // diagonal minus its weak connections.
        value_type dia = math::zero<value_type>();
        for (ptrdiff_t j = A.ptr[i], e = A.ptr[i + 1]; j < e; ++j) {
          if (A.col[j] == i || !aggr.strong_connection[j])
            dia += A.val[j];
        }
        if (!math::is_zero(dia))
          dia = -omega * math::inverse(dia);
 
        ptrdiff_t row_beg = P->ptr[i];
        ptrdiff_t row_end = row_beg;
        for (ptrdiff_t ja = A.ptr[i], ea = A.ptr[i + 1]; ja < ea; ++ja) {
          ptrdiff_t ca = A.col[ja];
 
          // Skip weak off-diagonal connections.
          if (ca != i && !aggr.strong_connection[ja])
            continue;
 
          value_type va = (ca == i)
          ? static_cast<value_type>(static_cast<scalar_type>(1 - omega) * math::identity<value_type>())
          : dia * A.val[ja];
 
          for (ptrdiff_t jp = P_tent->ptr[ca], ep = P_tent->ptr[ca + 1]; jp < ep; ++jp) {
            ptrdiff_t cp = P_tent->col[jp];
            value_type vp = P_tent->val[jp];
 
            if (marker[cp] < row_beg) {
              marker[cp] = row_end;
              P->col[row_end] = cp;
              P->val[row_end] = va * vp;
              ++row_end;
            }
            else {
              P->val[marker[cp]] += va * vp;
            }
          }
        }
      }
    });
    ARCCORE_ALINA_TOC("smoothing");
 
    return std::make_tuple(P, transpose(*P));
  }
 
  template <class Matrix>
  std::shared_ptr<Matrix>
  coarse_operator(const Matrix& A, const Matrix& P, const Matrix& R) const
  {
    return detail::galerkin(A, P, R);
  }
 
  params prm;
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
template <class Backend>
struct SmoothedAggregationEnergyMinCoarsening
{
  typedef pointwise_aggregates Aggregates;

  struct params
  {
    Aggregates::params aggr;

    nullspace_params nullspace;
 
    params() {}
 
    params(const PropertyTree& p)
    : ARCCORE_ALINA_PARAMS_IMPORT_CHILD(p, aggr)
    , ARCCORE_ALINA_PARAMS_IMPORT_CHILD(p, nullspace)
    {
      p.check_params( { "aggr", "nullspace" });
    }
 
    void get(PropertyTree& p, const std::string& path) const
    {
      ARCCORE_ALINA_PARAMS_EXPORT_CHILD(p, path, aggr);
      ARCCORE_ALINA_PARAMS_EXPORT_CHILD(p, path, nullspace);
    }
  } prm;
 
  SmoothedAggregationEnergyMinCoarsening(const params& prm = params())
  : prm(prm)
  {}
 
  template <class Matrix>
  std::tuple<std::shared_ptr<Matrix>, std::shared_ptr<Matrix>>
  transfer_operators(const Matrix& A)
  {
    typedef typename backend::value_type<Matrix>::type Val;
    typedef typename backend::col_type<Matrix>::type Col;
    typedef typename backend::ptr_type<Matrix>::type Ptr;
    typedef ptrdiff_t Idx;
 
    ARCCORE_ALINA_TIC("aggregates");
    Aggregates aggr(A, prm.aggr, prm.nullspace.cols);
    prm.aggr.eps_strong *= 0.5;
    ARCCORE_ALINA_TOC("aggregates");
 
    ARCCORE_ALINA_TIC("interpolation");
    auto P_tent = tentative_prolongation<Matrix>(backend::nbRow(A), aggr.count, aggr.id, prm.nullspace, prm.aggr.block_size);
 
    // Filter the system matrix
    CSRMatrix<Val, Col, Ptr> Af;
    Af.set_size(backend::nbRow(A), backend::nbColumn(A));
    Af.ptr[0] = 0;
 
    UniqueArray<Val> dia(Af.nbRow());
 
    arccoreParallelFor(0, Af.nbRow(), ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (Idx i = begin; i < (begin + size); ++i) {
        Idx row_begin = A.ptr[i];
        Idx row_end = A.ptr[i + 1];
        Idx row_width = row_end - row_begin;
 
        Val D = math::zero<Val>();
        for (Idx j = row_begin; j < row_end; ++j) {
          Idx c = A.col[j];
          Val v = A.val[j];
 
          if (c == i)
            D += v;
          else if (!aggr.strong_connection[j]) {
            D += v;
            --row_width;
          }
        }
 
        dia[i] = D;
        Af.ptr[i + 1] = row_width;
      }
    });
 
    Af.set_nonzeros(Af.scan_row_sizes());
 
    arccoreParallelFor(0, Af.nbRow(), ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (Idx i = begin; i < (begin + size); ++i) {
 
        Idx row_begin = A.ptr[i];
        Idx row_end = A.ptr[i + 1];
        Idx row_head = Af.ptr[i];
 
        for (Idx j = row_begin; j < row_end; ++j) {
          Idx c = A.col[j];
 
          if (c == i) {
            Af.col[row_head] = i;
            Af.val[row_head] = dia[i];
            ++row_head;
          }
          else if (aggr.strong_connection[j]) {
            Af.col[row_head] = c;
            Af.val[row_head] = A.val[j];
            ++row_head;
          }
        }
      }
    });
 
    UniqueArray<Val> omega;
 
    auto P = interpolation(Af, dia, *P_tent, omega);
    auto R = restriction(Af, dia, *P_tent, omega);
    ARCCORE_ALINA_TOC("interpolation");
 
    return std::make_tuple(P, R);
  }
 
  template <class Matrix>
  std::shared_ptr<Matrix>
  coarse_operator(const Matrix& A, const Matrix& P, const Matrix& R) const
  {
      return detail::galerkin(A, P, R);
  }
 
 private:
 
  template <class AMatrix, typename Val, typename Col, typename Ptr>
  static std::shared_ptr<CSRMatrix<Val, Col, Ptr>>
  interpolation(const AMatrix& A, const UniqueArray<Val>& Adia,
                const CSRMatrix<Val, Col, Ptr>& P_tent,
                UniqueArray<Val>& omega)
  {
    const size_t n = backend::nbRow(P_tent);
    const size_t nc = backend::nbColumn(P_tent);
 
    auto AP = product(A, P_tent, /*sort rows: */ true);
 
    omega.resize(nc, math::zero<Val>());
    UniqueArray<Val> denum(nc, math::zero<Val>());
 
    // TODO CONCURRENCY: remove these mutex
    std::mutex mutex1;
    std::mutex mutex2;
 
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      UniqueArray<ptrdiff_t> marker(nc, -1);
 
      // Compute A * Dinv * AP row by row and compute columnwise
      // scalar products necessary for computation of omega. The
      // actual results of matrix-matrix product are not stored.
      UniqueArray<Col> adap_col(128);
      UniqueArray<Val> adap_val(128);
 
      for (ptrdiff_t ia = begin; ia < (begin + size); ++ia) {
        adap_col.clear();
        adap_val.clear();
 
        // Form current row of ADAP matrix.
        for (auto a = A.row_begin(ia); a; ++a) {
          Col ca = a.col();
          Val va = math::inverse(Adia[ca]) * a.value();
 
          for (auto p = AP->row_begin(ca); p; ++p) {
            Col c = p.col();
            Val v = va * p.value();
 
            if (marker[c] < 0) {
              marker[c] = adap_col.size();
              adap_col.push_back(c);
              adap_val.push_back(v);
            }
            else {
              adap_val[marker[c]] += v;
            }
          }
        }
 
        if (adap_col.size() != 0)
          detail::sort_row(&adap_col[0], &adap_val[0], adap_col.size());
 
        // Update columnwise scalar products (AP,ADAP) and (ADAP,ADAP).
        // 1. (AP, ADAP)
        for (
        Ptr ja = AP->ptr[ia], ea = AP->ptr[ia + 1],
            jb = 0, eb = adap_col.size();
        ja < ea && jb < eb;) {
          Col ca = AP->col[ja];
          Col cb = adap_col[jb];
 
          if (ca < cb)
            ++ja;
          else if (cb < ca)
            ++jb;
          else /*ca == cb*/ {
            Val v = AP->val[ja] * adap_val[jb];
            {
              std::scoped_lock lock(mutex1);
              omega[ca] += v;
              ++ja;
              ++jb;
            }
          }
        }
 
        // 2. (ADAP, ADAP) (and clear marker)
        for (size_t j = 0, e = adap_col.size(); j < e; ++j) {
          Col c = adap_col[j];
          Val v = adap_val[j];
          {
            std::scoped_lock lock(mutex2);
            denum[c] += v * v;
            marker[c] = -1;
          }
        }
      }
    });
 
    for (size_t i = 0, m = omega.size(); i < m; ++i)
      omega[i] = math::inverse(denum[i]) * omega[i];
 
    // Update AP to obtain P: P = (P_tent - D^-1 A P Omega)
    /*
     * Here we use the fact that if P(i,j) != 0,
     * then with necessity AP(i,j) != 0:
     *
     * AP(i,j) = sum_k(A_ik P_kj), and A_ii != 0.
     */
    arccoreParallelFor(0, n, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        Val dia = math::inverse(Adia[i]);
 
        for (Ptr ja = AP->ptr[i], ea = AP->ptr[i + 1],
                 jp = P_tent.ptr[i], ep = P_tent.ptr[i + 1];
             ja < ea; ++ja) {
          Col ca = AP->col[ja];
          Val va = -dia * AP->val[ja] * omega[ca];
 
          for (; jp < ep; ++jp) {
            Col cp = P_tent.col[jp];
            if (cp > ca)
              break;
 
            if (cp == ca) {
              va += P_tent.val[jp];
              break;
            }
          }
 
          AP->val[ja] = va;
        }
      }
    });
 
    return AP;
  }
 
  template <typename AMatrix, typename Val, typename Col, typename Ptr>
  static std::shared_ptr<CSRMatrix<Val, Col, Ptr>>
  restriction(const AMatrix& A, const UniqueArray<Val>& Adia,
              const CSRMatrix<Val, Col, Ptr>& P_tent,
              const UniqueArray<Val>& omega)
  {
    const size_t nc = backend::nbColumn(P_tent);
 
    auto R_tent = transpose(P_tent);
    sort_rows(*R_tent);
 
    auto RA = product(*R_tent, A, /*sort rows: */ true);
 
    // Compute R = R_tent - Omega R_tent A D^-1.
    /*
     * Here we use the fact that if R(i,j) != 0,
     * then with necessity RA(i,j) != 0:
     *
     * RA(i,j) = sum_k(R_ik A_kj), and A_jj != 0.
     */
    arccoreParallelFor(0, nc, ForLoopRunInfo{}, [&](Int32 begin, Int32 size) {
      for (ptrdiff_t i = begin; i < (begin + size); ++i) {
        Val w = omega[i];
 
        for (Ptr ja = RA->ptr[i], ea = RA->ptr[i + 1],
                 jr = R_tent->ptr[i], er = R_tent->ptr[i + 1];
             ja < ea; ++ja) {
          Col ca = RA->col[ja];
          Val va = -w * math::inverse(Adia[ca]) * RA->val[ja];
 
          for (; jr < er; ++jr) {
            Col cr = R_tent->col[jr];
            if (cr > ca)
              break;
 
            if (cr == ca) {
              va += R_tent->val[jr];
              break;
            }
          }
 
          RA->val[ja] = va;
        }
      }
    });
 
    return RA;
  }
};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
} // namespace Arcane::Alina
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
namespace Arcane::Alina::backend
{
 
template <class Backend>
struct coarsening_is_supported<Backend, RugeStubenCoarsening,
                               typename std::enable_if<!std::is_arithmetic<typename backend::value_type<Backend>::type>::value>::type> : std::false_type
{};
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
} // namespace Arcane::Alina::backend
 
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
#endif