I'm looking for a reference for a matrix-norm inequality that I used in this answer, which has a few equivalent forms. I will use notation that applies to complex vector spaces with a sesquilinear inner product, but of course the same applies over real matrices.
The statement is as follows:
Take $A,B \in \Bbb F^{n \times n}$. Then
$$\vert\operatorname{tr}(A^*B)\vert \leq \sigma_1(A)\sum_{i=1}^n \sigma_i(B) = \|A\| \operatorname{tr}|B|$$
where $\sigma_i$ denotes the $i$th singular value, $|B| = (B^*B)^{1/2}$, and $\|\cdot\|$ denotes the spectral norm (induced Euclidean norm).
I did manage to find some references, but they're overkill, and the texts themselves are not readily accessible to the faint of heart (Bhatia's text is dense and Pedersen's is not about matrices in particular).
A suitable reference would be greatly appreciated.
Best Answer
A proof in linear algebra. I hope you're familiar with SVD.
Proof: By SVD decomposition, and properties of the trace function $$tr(A) = tr(U\Sigma V) = tr(\Sigma VU) $$ If $Z=VU$ then it is still an unitary matrix, and $$|tr(\Sigma Z)| = |\sum_i \sigma_i(A)z_{ii}|\le \sum_i |\sigma_i(A)z_{ii}|\le \sum_i \sigma_i(A) $$ since $|z_{ii}|\le 1$.
Proof: Using Fischer minmax theorem, we know $$ \sigma_i(A^*B) = \max_{\dim V=i}\min_{x\in V,\,\|x\|=1}\|A^*Bx\| $$ but $$ \min_{x\in V,\,\|x\|=1} \|A^*Bx\| \le \max_{x\in V,\,\|x\|=1}\|Bx\| \min_{y\in BV,\,\|y\|=1}\|A^*y\| $$ so $$ \sigma_i(A^*B) \le \max_{\dim V=i}(\max_{x\in V,\,\|x\|=1}\|Bx\| \min_{y\in BV,\,\|y\|=1}\|A^*y\|) $$ $$ \le \max_{\dim V=i}\max_{x\in V,\,\|x\|=1}\|Bx\| \max_{\dim V=i}\min_{y\in BV,\,\|y\|=1}\|A^*y\| \le \sigma_1(B)\sigma_i(A^*) $$