I read in a paper that the matrix minimization problem
$$ \mathbf{f(A)} = \min_{~~~~U,V\\\mathbf{A = UV'}} \max_i \mathbf{\|U_i\|} \max_i \mathbf{\|V_i\|} \\ \text{where } \mathbf{U_i} ~\text{and}~ \mathbf{V_i} ~ \text{are row vectors and } \|\cdot\| ~\text{is the}~ \ell_2 ~\text{norm} $$
is equivalent to the below SDP
$$ \min ~\eta\\ X_{ii} \leq \eta \quad \forall i\\ X \succeq 0\\ X\circ B = F $$
here
$$ B = \begin{bmatrix} 0 & A\\\ A^T & 0\end{bmatrix} \; \text{and} \\ F = \begin{bmatrix} 0 & \mathbf{1} \\ \mathbf{1} & 0\end{bmatrix} $$ $\mathbf{1}$ is the matrix of all ones and $\circ$ is the Hadamard product.
I do not get it. How can I get the SDP from the original problem ?