\varSigma})= \begin{pmatrix} 0 &amp;
\mathrm{diag} (\boldsymbol{\varSigma})^\top \\
\mathrm{diag}(\boldsymbol{\varSigma}) &amp;
\boldsymbol{1}\mathrm{diag}(\boldsymbol{\varSigma})^\top
+ \mathrm{diag}(\boldsymbol{\varSigma})\boldsymbol{1}^\top - 2
\boldsymbol{\varSigma}
\end{pmatrix}
\end{align*}\]</span> one can perform unconstrained optimization for the
non-zero elements of <span class="math inline">\(\mathbf{L}\)</span>
which are in one-to-one correspondence with those of <span class="math inline">\(\bol