Graduate Commutation and Non-Linear Operators

Click For Summary
The discussion centers on the properties of a defined non-linear operator, denoted as ⟨A⟩, and its relationship with linear operators A and B. Participants explore whether the equations ⟨A⟩A = A⟨A⟩ and ⟨A⟩B = B⟨A⟩ hold true, concluding that they do when ⟨A⟩ is treated as a scalar resulting from the inner product ⟨x|A|x⟩. There is confusion regarding the nature of ⟨A⟩ as a nonlinear operator versus a scalar, with clarifications that it behaves as a linear operator when multiplied by vectors. The conversation also touches on implications for Heisenberg's uncertainty principle, emphasizing the distinction between the scalar average and the operator notation used in quantum mechanics. Overall, the thread highlights the nuances in operator algebra and the potential for misinterpretation in mathematical notation.
MisterX
Messages
758
Reaction score
71
Suppose ##A## is a linear operator ##V\to V## and ##\mathbf{x} \in V##. We define a non-linear operator ##\langle A \rangle## as $$\langle A \rangle\mathbf{x} := <\mathbf{x}, A\mathbf{x}>\mathbf{x}$$

Can we say ## \langle A \rangle A = A\langle A \rangle ##? What about ## \langle A \rangle B = B\langle A \rangle ## ?

More generally if we have ##Q\mathbf{x} = q(\mathbf{x}) \mathbf{x}## with scalar function ##q##, when does ##AQ=QA ## ? I assert we can choose ##A, Q## such that this is false.
 
Physics news on Phys.org
MisterX said:
Can we say ## \langle A \rangle A = A\langle A \rangle ##? What about ## \langle A \rangle B = B\langle A \rangle ## ?
The way to test this is to expand ##(\langle A\rangle B)\mathbf x = \langle A \rangle (Bx)## and ##(B\langle A\rangle)\mathbf x = B( \langle A \rangle x)## using your definitions above to see whether they give exactly the same result.
 
I guess I am not sure about the use of the associative property here. But ignoring this it seems
## \langle A \rangle (B\mathbf {x}) = <B\mathbf {x}, AB\mathbf {x}>B\mathbf {x} ## and ##B( \langle A \rangle \mathbf {x})= <\mathbf {x}, A \mathbf {x}>B\mathbf {x}\neq \langle A \rangle (B\mathbf {x}) ##.

This is troubling to me because this step seems to be used in the proof of Heisenberg's uncertainty principle. Sources claim $$[A-\langle A \rangle, \, B- \langle B \rangle ] = [A,\,B] $$
I guess now am not sure if this is true and why.
 
That's because in that Heisenberg formula ##\langle A\rangle## is a scalar, not a nonlinear operator. The only similarity with this problem is the use of angle bracket symbols, but you have used them to mean something completely different from what Heisenberg means by them.

Where did you get the notion that ##\langle A\rangle## is a nonlinear operator?
 
andrewkirk said:
That's because in that Heisenberg formula ##\langle A\rangle## is a scalar, not a nonlinear operator. The only similarity with this problem is the use of angle bracket symbols, but you have used them to mean something completely different from what Heisenberg means by them.

Where did you get the notion that ##\langle A\rangle## is a nonlinear operator?
Are you asserting that ##\langle A\rangle## is not 2nd order in ##\mathbf{x}##? Doesn't the average of an operator depend on the state on which it acts?
 
MisterX said:
Doesn't the average of an operator depend on the state on which it acts?
That's right. To be precise, ##\langle A \rangle## denotes the scalar value ##\langle \mathbf x\ |\ A\ |\ \mathbf x\rangle## where ##\mathbf x## is the current state.

But ##\langle A \rangle## denotes the scalar result, not an operator. It is only an operator in the sense that scalar multiplication is a linear operation. If that is the meaning in your OP then the answers are, treating ##\langle A \rangle## as the linear operator that pre-multiplies any vector ##\mathbf y## by the scalar ##\langle A \rangle## (which is ##\langle \mathbf x\ |\ A \ |\ \mathbf x\rangle##), that both of the following are true:
  • ##\langle A\rangle A= A\langle A\rangle##
  • ##\langle A\rangle B= B\langle A\rangle##
since scalar multiplication commutes with any linear operator.

But we need to note that ##\langle A\rangle A \mathbf y## is
##\langle \mathbf x\ |\ A \ |\ \mathbf x\rangle A\mathbf y##
not
##\langle \mathbf y\ |\ A \ |\ \mathbf y\rangle A\mathbf y##

It has to be admitted, the standard notation is a little confusing because ##\langle A \rangle## depends on ##\mathbf x## even though that does not appear anywhere in the notation.
 
  • Like
Likes StoneTemplePython
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 2 ·
Replies
2
Views
3K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 4 ·
Replies
4
Views
1K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 21 ·
Replies
21
Views
3K
  • · Replies 2 ·
Replies
2
Views
1K
  • · Replies 0 ·
Replies
0
Views
1K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 1 ·
Replies
1
Views
965
  • · Replies 2 ·
Replies
2
Views
2K