Vectorization and inner product of matrices

We know that the Frobenius inner product $$A \cdot B = tr(A^\top B)$$ between two $$n\times n$$ matrices $$A$$ and $$B$$ can be represented as $$A \cdot B = \langle\vec A, \vec B\rangle = tr \vec A (\vec B)^\top$$ where on the right side $$\langle \cdot, \rangle$$ is the ordinary Euclidean scalar product and where $$\vec X$$ denotes the vectorization of $$X$$. I am curious about (and would have use of) a slightly more general case.

In particular, I have matrices $$Q,A,B$$ where I want to use positive definite matrix $$Q$$ to "skew" the inner product, i.e., I am interest in the rewriting the quantity $$tr (Q A^\top B)$$ in terms of $$\vec A$$ and $$\vec B$$ as is possible when $$Q=I$$.

My question is thus: Can we write $$tr (QA^\top B) = \langle \vec A, \vec B \rangle_Q$$ where $$\langle\cdot, \cdot \rangle_Q$$ is a skewed euclidean inner product on $$\mathbb{R} ^{n^2}$$, depending on $$Q$$?

My guess would be that this is indeed possible and should look something like $$tr (QA^\top B) = tr \left( (I \otimes Q) (\vec A \otimes (\vec B)^\top))\right)$$ but I am certainly not sure and don't really know how to proceed.

I would very much appreciate help in finding a rewrite of the quantity $$tr (QA^\top B)$$which is similar to this.

Edit for clarity:

The question could be reformulated as: Is there a euclidean inner product $$\langle , \rangle _Q$$ weighted by some matrix (say $$M=M(Q)$$), such that $$tr( QA^\top B) = \langle \vec A, \vec B\rangle_Q=\langle M(Q) \vec A,\vec B\rangle$$? In the end what I am after is to express $$tr QA^\top B$$ in terms of the trace of the outer product of $$\vec A$$ and $$\vec B$$.

Progress Update:

I was able to find a reference ( https://www.ime.unicamp.br/~cnaber/Kronecker.pdf) online which states that

$$tr (ABC ) = (\vec A^\top)^\top (I\otimes B) \vec C$$

which is more or less equivalent to what I am asking. However, as the reference does include a proof, I would still like this to be resolved.

• How do you mean skew? As in a prescribed weight for position of each element? If so, then I think this won't work but you will need to work in vectorized space. But maybe you are happy to only be able to skew different rows differently? Then I think it will work. Oct 25, 2019 at 11:58
• By skew I am referring to the premultplication by the matrix $Q$ in the trace. Oct 25, 2019 at 12:01
• Ok, you can for sure get something more general with $vec(A)^T Q vec(B)$, where $Q$ is big diagonal matrix with one diagonal entry per matrix entry in each $A$ and $B$ but maybe you don't need it. Yes Kronecker product with identity matrix shall also help you get what you want. I think there is a good wikipedia on it. en.wikipedia.org/wiki/Kronecker_product#Matrix_equations Oct 25, 2019 at 13:29

For ease of typing, let's use $$A:B$$ instead of $$A\cdot B$$ to denote the Frobenius inner product,
and $$a$$ instead of $$\vec A$$ to denote vectorization, i.e. \eqalign{ A:B &= {\rm Tr}(A^TB) = &{\rm Tr}(B^TA) = B:A \\ A:B &= a:b &({\rm Frobenius\,product\,notation})\\ A:B &= a^Tb &({\rm Matrix\,product\,notation})\\ } Using this we can develop various expressions for the following scalar quantity \eqalign{ {\rm Tr}(QA^TB) &= {\rm Tr}(A^TBQ) \\ &= A:BQ \\ &= {\rm vec}(A):{\rm vec}(IBQ) \\ &= a:(Q^T\otimes I)\,b \\ &= a^T(Q^T\otimes I)\,b \\ &= b^T(Q\otimes I)\,a \\ }