Refined Heinz-Kato-L\"owner inequalities

Abstract

A version of the Cauchy-Schwarz inequality in operator theory is the following: for any two symmetric, positive definite matrices $A,B \in \mathbb{R}^{n \times n}$ and arbitrary $X \in \mathbb{R}^{n \times n}$ $$ \|AXB\| \leq \|A^2 X\|^{\frac{1}{2}} \|X B^2\|^{\frac{1}{2}}.$$ This inequality is classical and equivalent to the celebrated Heinz-L\"owner, Heinz-Kato and Cordes inequalities. We characterize cases of equality: in particular, after factoring out the symmetry coming from multiplication with scalars $ \|A^2 X\| = 1 = \|X B^2\|$, the case of equality requires that $A$ and $B$ have a common eigenvalue $\lambda_i = \mu_j$. We also derive improved estimates and show that if either $\lambda_i \lambda_j = \mu_k^2$ or $\lambda_i^2 = \mu_j \mu_k$ does not have a solution, i.e. if $d > 0$ where \begin{align*} d &= \min_{1 \leq i,j,k \leq n} \{ | \log{ \lambda_i} + \log{ \lambda_j} - 2\log{ \mu_k}|:\lambda_i, \lambda_j \in \sigma(A), \mu_k \in \sigma(B) \} &+\min_{1 \leq i,j,k \leq n}\{ | 2\log{\lambda_i} - \log{ \mu_j} - \log{\mu_k } |:\lambda_i \in \sigma(A), \mu_j, \mu_k \in \sigma(B) \}, \end{align*} then there is an improved inequality $$ \|AXB\| \leq (1 - c_{n,d})\|A^2 X\|^{\frac{1}{2}} \|X B^2\|^{\frac{1}{2}}$$ for some $c_{n,d} > 0$ that only depends only on $n$ and $d$. We obtain similar results for the McIntosh inequality and the Cordes inequality and expect the method to have many further applications.