The portfolio weights vector of the minimum-variance portfolio has a closed-form analytical solution,
$$\boldsymbol{w} = \frac{\boldsymbol{\Sigma}^{-1} \boldsymbol{1} }{\boldsymbol{1}^\top \boldsymbol{\Sigma}^{-1} \boldsymbol{1}}$$
but is there a direct calculation for the same portfolio's variance $\sigma_p^2$?
Given that $ \sigma_p^2 = \boldsymbol{w^\top \Sigma w}$, what is the simplification of
\begin{aligned} \sigma_p^2 & = \left( \frac{\boldsymbol{\Sigma}^{-1} \boldsymbol{1} }{\boldsymbol{1}^\top \boldsymbol{\Sigma}^{-1} \boldsymbol{1}}\right)^\top \cdot \boldsymbol{\Sigma} \cdot \frac{\boldsymbol{\Sigma}^{-1} \boldsymbol{1} }{\boldsymbol{1}^\top \boldsymbol{\Sigma}^{-1} \boldsymbol{1}} \\ & = \frac{\boldsymbol{1} ^\top(\boldsymbol{\Sigma}^\top)^{-1}}{\boldsymbol{1} ^\top\boldsymbol{\Sigma}^{-1} \boldsymbol{1} } \cdot \boldsymbol{\Sigma} \cdot \frac{\boldsymbol{\Sigma}^{-1} \boldsymbol{1} }{\boldsymbol{1}^\top \boldsymbol{\Sigma}^{-1} \boldsymbol{1}} \\ & = ? \end{aligned}
$$$$
How about the maximum-Sharpe ratio portfolio's variance as well?