Adjoint transformation (inverse)

In summary, the definition of an adjoint transformation states that iff \overline{f}( \textbf{a}) \ast \textbf{b} = \textbf{a} \ast f(\textbf{b}) and the inverse of everything is also an adjoint transformation. It is said that this property is easily shown from the definition, but I cannot seem to prove it. If you follow the steps I described, you should be able to get there.
  • #1
mnb96
715
5
Hi,
in the text I am reading I found the following implicit definition of an adjoint transformation:

[tex]\overline{f}( \textbf{a}) \ast \textbf{b} = \textbf{a} \ast f(\textbf{b})[/tex]

then it is said that [tex]\overline{f^{-1}} = (\overline{f})^{-1}[/tex]. Basically the inverse and ajoint are interchangeable, and this property is (supposed to be) easily shown from the definition above.
Unfortunately I have problems figuring out how to prove it. Any ideas?
 
Physics news on Phys.org
  • #2
it's been a really long time since i did anything math related, but i think if you * the inverse of everything to both sides (i.e. step 1, * (f^bar(a))^-1 to both sides, step 2 * b^1 to both sides, step 3 * a^1 to both sides, step 4 * (f(b))^-1 to both sides) you should end up with

[tex]
\textbf{a}^{-1} \ast f(\textbf{b})^{-1} = \textbf{b}^{-1} \ast \overline{f}( \textbf{a})^{-1}
[/tex]

does that give you what you want? sorry, it's been a long time since I've done this type of stuff. i think this is probably wrong, but it may give you a step in the right direction.
 
  • #3
When you say, step1 * f^bar(a))^-1 to both sides do you mean the following?

[tex]\overline{f}\\^{-1}(\textbf{a}) \ast \overline{f}( \textbf{a}) \ast \textbf{b} = \overline{f}\\^{-1}(\textbf{a}) \ast \textbf{a} \ast f(\textbf{b})
[/tex]
 
  • #4
actually i meant this:

[tex]
(\overline{f}\\(\textbf{a}))^{-1} \ast \overline{f}( \textbf{a}) \ast \textbf{b} = (\overline{f}\\(\textbf{a}))^{-1} \ast \textbf{a} \ast f(\textbf{b})

[/tex]

i'm not that great with latex, or i would have tried to explain it better; my apologies. i think the equation in my post is actually incorrect now that i look at it, but if you follow the method i described i think it gets you somewhere.

after you simplify you get

[tex]
\textbf{b} = (\overline{f}\\(\textbf{a}))^{-1} \ast \textbf{a} \ast f(\textbf{b})

[/tex]

and then you continue to * inverses on both sides (i don't want to say multiply because * is an operator not always multiplication... is there a better word for that?)
 
  • #5
I found a solution when the operation * is cancellative (which I can assume to be my case):

[tex]\overline{f^{-1}}(a) * f(b) = a * f^{-1}(f(b)) = a * b = \overline{f}(\overline{f}^{-1}(a)) * b = \overline{f}^{-1}(a) * f(b)[/tex]

However if you check from wikipedia http://en.wikipedia.org/wiki/Hermitian_adjoint#Properties this seems to be a property of linear operators, but I wonder how you can prove it without assuming cancellativity ([tex]y*x = z*x \\ \Rightarrow \\ y=z[/tex])
 

FAQ: Adjoint transformation (inverse)

What is an adjoint transformation?

An adjoint transformation, also known as the inverse transformation, is a mathematical operation that reverses the effects of a given transformation. It is used to find the original input values that produced a given output.

How is an adjoint transformation calculated?

The calculation of an adjoint transformation depends on the specific transformation being used. In general, it involves finding the inverse of the matrix representing the transformation. The resulting matrix will be the adjoint transformation.

What is the significance of an adjoint transformation?

An adjoint transformation is important in various fields of science and mathematics, such as linear algebra, differential equations, and quantum mechanics. It allows for the solution of inverse problems and is essential in understanding the behavior of dynamic systems.

Can an adjoint transformation be applied to non-linear transformations?

No, an adjoint transformation can only be applied to linear transformations. Non-linear transformations do not have inverse operations, so an adjoint transformation cannot be calculated for them.

How is an adjoint transformation used in data analysis?

In data analysis, an adjoint transformation is used to find the original input values that produced a given set of data. This can help in understanding the relationship between different variables and can be used for prediction and optimization purposes.

Similar threads

Replies
4
Views
2K
Replies
6
Views
2K
Replies
3
Views
3K
Replies
6
Views
2K
Replies
1
Views
2K
Back
Top