Given a matrix $F \in \mathbb{C}^{m \times n}$ such that $m > n$ and other (non-symmetric) square matrix $A$ of size $n \times n$, how can one formulate
$$ \arg \min_b \left\|A- {F}^{*} \operatorname{diag} \left( b \right) \, {F} \right\|_{2}$$
where $b \in \mathbb{C}^m$ is some vector and $*$ denotes the conjugate transpose, as a semidefinite program?
I started as follows. Writing the above problem in epigraph form by introducing a variable $x$,
\begin{array}{ll} \text{minimize} & x\\ \text{subject to} & \left\|A- {F}^{*} \operatorname{diag} \left( b \right) \, {F} \right\|_{2} \leq x\end{array}
which is equivalent to
\begin{array}{ll} \text{minimize} & x\\ \text{subject to} & \sigma_{\max}(A- {F}^{*} \operatorname{diag} \left( b \right) \, {F} ) \leq x\end{array}
which is equivalent to
\begin{array}{ll} \text{minimize} & x\\ \text{subject to} & \lambda_{\max}\big((A- {F}^{*} \operatorname{diag} \left( b \right) \, {F} )^*(A- {F}^{*} \operatorname{diag} \left( b \right) \, {F} ) \big) \leq x^2\end{array}
Can anybody tell me how I can proceed with this?