Method collapseDuplicates

include/Eigen/Eigen/src/SparseCore/SparseMatrix.h:1506–1542

template <typename Scalar_, int Options_, typename StorageIndex_>
template <typename Derived, typename DupFunctor>
void SparseMatrix<Scalar_, Options_, StorageIndex_>::collapseDuplicates(DenseBase<Derived>& wi, DupFunctor dup_func) {
  // removes duplicate entries and compresses the matrix
  // the excess allocated memory is not released
  // the inner indices do not need to be sorted, nor is the matrix returned in a sorted state
  eigen_assert(wi.size() == m_innerSize);
  constexpr StorageIndex kEmptyIndexValue(-1);
  wi.setConstant(kEmptyIndexValue);
  StorageIndex count = 0;
  const bool is_compressed = isCompressed();
  // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
  for (Index j = 0; j < m_outerSize; ++j) {
    const StorageIndex newBegin = count;
    const StorageIndex end = is_compressed ? m_outerIndex[j + 1] : m_outerIndex[j] + m_innerNonZeros[j];
    for (StorageIndex k = m_outerIndex[j]; k < end; ++k) {
      StorageIndex i = m_data.index(k);
      if (wi(i) >= newBegin) {
        // entry at k is a duplicate
        // accumulate it into the primary entry located at wi(i)
        m_data.value(wi(i)) = dup_func(m_data.value(wi(i)), m_data.value(k));
      } else {
        // k is the primary entry in j with inner index i
        // shift it to the left and record its location at wi(i)
        m_data.index(count) = i;
        m_data.value(count) = m_data.value(k);
        wi(i) = count;
        ++count;
      }
    }
    m_outerIndex[j] = newBegin;
  }
  m_outerIndex[m_outerSize] = count;
  m_data.resize(count);

  // turn the matrix into compressed form (if it is not already)
  internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
  m_innerNonZeros = 0;
}

/** \internal */
template<typename Scalar, int Options_, typename StorageIndex_>