Undergrad bra-ket with adjoints identity

Click For Summary
The discussion centers on the adjoint identity in the context of bra-ket notation, specifically the equation $\langle v|A|w\rangle = \langle w|A^{\dagger}|v \rangle^*$. A user expresses confusion over the application of this identity with real matrices and presents calculations that demonstrate the relationship between inner products and adjoint operators. The example provided shows that the inner product $\langle A(x), y \rangle$ equals $\langle x, A^\dagger(y) \rangle$, confirming the adjoint relationship. The user realizes that their previous calculations misinterpreted the associativity of matrix multiplication. The discussion highlights the importance of understanding adjoint operations in linear algebra.
nomadreid
Gold Member
Messages
1,762
Reaction score
248
TL;DR
I thought that, if B is the adjoint of A, <v|A|w>=<v|(A|w>)=<(v|B)|w>. But a simple example with real matrices trips me up.
Continuing the summary: the example in question is
adjoint.png

Obviously I am understanding some extremely elementary point incorrectly. What? Many thanks!
 
Physics news on Phys.org
The correct equation is:
$$\langle v|A|w\rangle = \langle w|A^{\dagger}|v \rangle^*$$
 
nomadreid said:
TL;DR Summary: I thought that, if B is the adjoint of A, <v|A|w>=<v|(A|w>)=<(v|B)|w>. But a simple example with real matrices trips me up.

Continuing the summary: the example in question is
View attachment 357730
Obviously I am understanding some extremely elementary point incorrectly. What? Many thanks!
The formula is
$$
\bigl\langle A(x),y \bigr\rangle = \bigl\langle x, A^\dagger (y) \bigr\rangle .
$$

This means for your example with ##x=(0,1)## and ##y=(3,4)## that
\begin{align*}
\bigl\langle A(x),y \bigr\rangle &=\bigl\langle \begin{pmatrix}1&2\\3&4\end{pmatrix}\begin{pmatrix}0\\1\end{pmatrix}\, , \,\begin{pmatrix}3\\4\end{pmatrix} \bigr\rangle =\bigl\langle \begin{pmatrix}2\\4\end{pmatrix}\, , \,\begin{pmatrix}3\\4\end{pmatrix} \bigr\rangle =22\\[12pt]
\bigl\langle x , A^\dagger (y)\bigr\rangle &= \bigl\langle \begin{pmatrix}0\\1\end{pmatrix}\, , \,\begin{pmatrix}1&3\\2&4\end{pmatrix}\begin{pmatrix}3\\4\end{pmatrix} \bigr\rangle =\bigl\langle \begin{pmatrix}0\\1\end{pmatrix}\, , \,\begin{pmatrix}15\\22\end{pmatrix} \bigr\rangle =22
\end{align*}
or the other way around
$$
\bigl\langle A^\dagger (x),y \bigr\rangle = \bigl\langle x, A(y) \bigr\rangle =25 .
$$
What you have calculated was the associativity of matrix multiplication with two different matrices:
##XAY \neq_{i.g.} XA^\dagger Y.##
 
  • Like
Likes nomadreid and PeroK
Many,many thanks, PeroK and fresh42!
 
Thread 'How to define a vector field?'
Hello! In one book I saw that function ##V## of 3 variables ##V_x, V_y, V_z## (vector field in 3D) can be decomposed in a Taylor series without higher-order terms (partial derivative of second power and higher) at point ##(0,0,0)## such way: I think so: higher-order terms can be neglected because partial derivative of second power and higher are equal to 0. Is this true? And how to define vector field correctly for this case? (In the book I found nothing and my attempt was wrong...

Similar threads

  • · Replies 8 ·
Replies
8
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 14 ·
Replies
14
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 6 ·
Replies
6
Views
1K
  • · Replies 15 ·
Replies
15
Views
2K
  • · Replies 2 ·
Replies
2
Views
3K
  • · Replies 6 ·
Replies
6
Views
3K
  • · Replies 20 ·
Replies
20
Views
9K
  • · Replies 7 ·
Replies
7
Views
2K