Let $A \in \mathbb{R}^{n \times n}$ a symmetrical and invertible Matrix, and let $\| \cdot \|$ be by the euclidian norm (2-norm) induced matrix norm. Furthermore, $\lambda_{min}(A^2)$ is the smallest Eigenvalue of $A^2$. Prove:
$$\|A^{-1}\|=\frac{1}{\sqrt{\lambda_{min}(A^2)}}$$
Well, I tried showing
$$\|A^{-1}\| \leq \frac{1}{\sqrt{\lambda_{min}(A^2)}}$$ and $$\|A^{-1}\| \geq \frac{1}{\sqrt{\lambda_{min}(A^2)}}$$
but to no avail.
Since $A$ is symmetrical and invertible, I know that the following equality with $X$ as a transformation holds. And furthermore, $A$ is positive definit, therefore, all Eigenvales must be real and positive.
$$\|A^{-1}\| = \|X \cdot D^{-1} \cdot X^{-1}\|$$
How should I tackle this proof? Thank you.