How to find $y_u?$

In the paper Semi-supervised learning by mixed label propagation Wei Tong and Rong Jin define

$$S$$ as the similarity(adjacency) matrix
$$D = \operatorname{diag}(D_1, D_2, \ldots, D_n)$$ where $$D_i = \sum_{j=1}^nS_{i,j}$$
class assignment $$\textbf{y} = (y_1, y_2, \ldots, y_n)$$

Given the class labels of $$n_l$$ examples, $$\widehat{y}_l = (\widehat{y}_1, \widehat{y}_2,\ldots,\widehat{y}_{nl})$$, the optimal class assignment y is found by minimizing the energy function

where $$E(S, \textbf{y}) = \sum_{i, j=1}^n S_{ij}(y_i - y_j)^2 = \textbf{y}^TL \textbf{y}, L = D - S$$. How to find $$y_u$$? I have tried the following.

\begin{align} E(S, \textbf{y}) & = \begin{bmatrix} y_l & y_u \end{bmatrix} \begin{bmatrix} L^{l, l} & L^{l, u}\\ L^{u, l} & L^{u, u}\\ \end{bmatrix} \begin{bmatrix} y_l \\ y_u \end{bmatrix} \\[6pt] & = \begin{bmatrix} y_l & y_u \end{bmatrix} \begin{bmatrix} L^{l, l}y_l + L^{l, u}y_u\\ L^{u, l}y_l + L^{u, u}y_u\\ \end{bmatrix} \\[6pt] & = y_l^TL^{l, l}y_l + y_l^TL^{l, u}y_u + y_u^TL^{u, l}y_l + y_u^TL^{u, u}y_u \end{align}

when I take the derivative w.r.t $$y_u$$ I don't understand how to differentiate $$y_u^TL^{u, l}y_l$$. Following this method,

$$(y_u + h)^TL^{u, l}y_l - y_u^TL^{u, l}y_l = h^TL^{u, l}y_l$$

Now, how to divide by $$h$$.

Also if this is not the correct way, how to find $$y_u$$ then?

As $$L$$ is symmetric, \begin{align} \frac{d (E(S, \textbf{y}))}{d y_u} = 0 + y_l^TL^{l,u} + (L^{u,l}y_l)^T + y_u^TL^{u, u} + (L^{u, u}y_u)^T = 0 \\ 2 y_l^T L^{L, u} + 2y_u^T L^{u, u} = 0 \\ y_u = -(L^{u,u})^{-1}L^{u,l}y_l \end{align}