// ----------------------------------------------------------------
explicit q_data(const matrix_type & d)
: d_ij ()
, d_ik ()
, d_jk ()
, i ()
, j ()
{
static const auto k_0_5 = value_type(0.5);
const auto n = d.get_height();
const auto k_n_2 = value_type(n - 2);
//
// Cache the row sums; column sums would work equally well
// because the matrix is symmetric.
//
std::vector<value_type> sigma;
for (size_t c = 0; c < n; c++)
sigma.push_back(d.get_row_sum(c));
//
// Compute the values of the Q matrix.
//
matrix_type q (n, n);
for (size_t r = 0; r < n; r++)
for (size_t c = 0; c < r; c++)
q(r, c) = k_n_2 * d(r, c) - sigma[r] - sigma[c];
//
// Find the cell with the minimum value.
//
i = 1, j = 0;
for (size_t r = 2; r < n; r++)
for (size_t c = 0; c < r; c++)
if (q(r, c) < q(i, j))
i = r, j = c;
//
// Compute distances between the new nodes.
//
d_ij = d(i, j);
d_ik = k_0_5 * (d_ij + ((sigma[i] - sigma[j]) / k_n_2));
d_jk = d_ij - d_ik;
}
