Bound for the norm of expectation of positive semi-definite operators composition

expected valuelinear algebrapositive-semidefiniteupper-lower-bounds

Let $\mathcal X: \mathbb R^{m\times n}\to \mathbb R^{m\times n}$ be a random positive semi-definite operator such that $\|\mathcal X\|\leq a$ where $\|\mathcal X\| = \max_A \|\mathcal X(A)\|_F/\|A\|_F$ is the spectral norm, and $a$ is some deterministic constant.
My question is whether the following inequality holds:
$$
\|E[\mathcal X^2]\| \leq \|E[\|\mathcal X\| \cdot \mathcal X]\| \leq a\cdot\|E[\mathcal X] \|.
$$

By the Cauchy-Schwarz inequality, it is immediate that $\|E[\mathcal X^2]\| \leq E[\|\mathcal X\|\|\mathcal X\|] \leq a E[\|\mathcal X\|]$. However, this is weaker than the desired inequality.

In a way, my question is if the scalar inequality $x\cdot y \leq x\cdot \max(y)$ for $x\geq 0$ generalizes, in some sense, to the positive semi-definite matrix case?

Best Answer

It is true. The given conditions imply that $\mathcal X^2\preceq\|\mathcal X\|\mathcal X\preceq a\mathcal X$ in positive semidefinite partial ordering. Hence $E(\mathcal X^2)\preceq E(\|\mathcal X\|\mathcal X)\preceq E(a\mathcal X)$ and in turn, $\|E(\mathcal X^2)\|\le\big\|E\big(\|\mathcal X\|\mathcal X\big)\big\|\le\|E(a\mathcal X)\|$.

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