Theorem 7.21 in [Vidyasagar, M. (2019). An introduction to compressed sensing. Society for Industrial and Applied Mathematics] appears to rely on the following claim.
Let $\mathcal{A} : R^{n_r \times n_c} \to R^m$ be linear and define $\mathcal{B} = \mathcal{A}^* \mathcal{A} - I$, where $\mathcal{A}^*$ is the adjoint of $\mathcal{A}$. Given a positive integer $k \leq \min \{n_r, n_c\}$, consider the set of unit-norm matrices with rank less than or equal to $k$, $$ S(k) = \{ X \in R^{n_r \times n_c} : \text{rank}(X) \leq k \text{ and } \| X \|_F = 1 \}. $$ Then, $$ \sup_{Z \in S(k)} | \langle Z, \mathcal{B}(Z) \rangle_F | = \sup_{Z \in S(k)} \| \mathcal{B}(Z) \|_F. $$
I am able to show that the LHS $\leq$ RHS. From the Cauchy-Schwarz inequality, $$ | \langle Z, \mathcal{B}(Z) \rangle_F | \leq \| Z \|_F \| \mathcal{B}(Z) \|_F. $$ Taking the supremum over $S(k)$ of both sides yields $$ \begin{aligned} \sup_{Z \in S(k)} | \langle Z, \mathcal{B}(Z) \rangle_F | &\leq \sup_{Z \in S(k)} \| Z \|_F \| \mathcal{B}(Z) \|_F \\ &= \sup_{Z \in S(k)} \| \mathcal{B}(Z) \|_F. \end{aligned} $$ However, demonstrating equality (e.g., via RHS $\leq$ LHS) is proving difficult for me. I initially tried to mimic the approach here, but ran into issues due to the rank restriction in $S(k)$. The text claims that $\mathcal{B}$ being self-adjoint is important.
Would someone be able to help me out?