A log-majorization inequality for normal matrices with applications to determinantal inequalities and geometric means

We establish a log-majorization inequality comparing the eigenvalues of the interlaced product $Y^t X^*Y^{1-t}X$ with those of $X^*YX$, valid for every positive semi-definite $Y$ and every normal $X$, with the inequality reversing for $t \notin[0,1]$ when $Y$ is positive definite.

This extends known Hermitian results to the strictly larger class of normal matrices, where normality is shown to be the exact structural hypothesis, not a technical convenience.

A counterexample proves the result can fail without it.

As applications, we settle a normal-matrix extension of a determinantal conjecture of Lin, proving $$\det(A^*A + |BA|^p) \le \det(AA^* + |A^*B^*|^p)$$ for arbitrary $A$, normal $B$ and $p \ge 0$, and we give a complete eigenvalue picture for products of weighted geometric means, sharpening and complementing a theorem of Hiai and Lin.