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Could someone explain this equation?

$$ \frac{d \operatorname{tr}(AXB)}{d X} = BA $$

I understand that

$$ d\operatorname{tr}(AXB) = \operatorname{tr}(BA \; dX) $$

but I don't quite understand how to move $dX$ out of the trace.

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2 Answers 2

The notation is quite misleading (at least for me).


Does it make sense that $$\frac{\partial}{\partial X_{mn}} \mathop{\rm tr} (A X B) = (B A)_{nm}?$$

More information: $$\frac{\partial}{\partial X_{mn}} \mathop{\rm tr} (A X B) = \frac{\partial}{\partial X_{mn}} \sum_{jkl} A_{jk} X_{kl} B_{lj} = \sum_{jkl} A_{jk} \delta_{km} \delta_{nl} B_{lj} = \sum_{j} A_{jm} B_{nj} =(B A)_{nm}. $$

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That does make sense if I assume the first equation of $\frac{dtr(AXB)}{dX}$. But I'm not sure how to get to the first equation using the second equation for $dtr(AXB)$. –  Chris D Mar 11 '12 at 5:37
@ChrisD: I added a line explaining how to use my hint. –  Fabian Mar 11 '12 at 12:41
I see what you mean by misleading notation. The resulting matrix is indexed by the transpose of the matrix you differentiate by, $X$. Therefore, the full matrix solution $BA$ is the transpose of the element by element solution $(BA)_{nm}$. Thanks for the help. –  Chris D Mar 11 '12 at 20:10

Try expanding to linear order. This always eases the understanding:

$$\operatorname{tr}(A (X+dX)B)=A_{ij} (X_{jk}+dX_{jk})B_{ki})$$

where Einstein's summation rule is used. Substracting $\operatorname{tr}(AXB)$ you get

$$\begin{align} d\operatorname{tr}(AXB)&=\operatorname{tr}(A(X+dX)B)-\operatorname{tr}(AXB)\\&=A_{ij} dX_{jk}B_{ki}=\underbrace{B_{ki}A_{ij}}_{=(BA)_{kj}} \; dX_{jk} \end{align}$$

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When you say 'expanding to linear order', do you mean write out the actual matrix element summations? I had to do that manually to prove $$\operatorname{tr}(A (X+dX)B)=A_{ij} (X_{jk}+dX_{jk})B_{ki})$$ to myself. Is that something that's easily derived without explicitly expanding the matrices or is this something people generally just memorize about traces? –  Chris D Mar 11 '12 at 20:32
What I meant was writing $X+dX$ and work with that, keeping only stuff that is linear in $dX$. In this case, everything was linear to begin with, so my comment was a bit misleading. But it is the right way to work when deriving more complicated tensorial derivatives like, say, $$\frac{\partial \det (A)}{\partial A}=\det(A) \left(A^{-1}\right)^T$$ –  yohBS Mar 12 '12 at 5:35

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