Show A = UDU(dagger) can be written as f(A) = Uf(D)U(dagger)

  • Thread starter Smazmbazm
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In summary, a diagonalisable square matrix A can be expressed as A = UDU\dagger, where U is a unitary matrix and D is diagonal. Using the definition of f(D) as a power series, it can be shown that f(A) = Uf(D)U\dagger. This can be done by rewriting Uf(D)U\dagger as a sum of linear operators and then using the definition of f(D).
  • #1
Smazmbazm
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Homework Statement



A diagonalisable square matrix A can be written [itex]A = UDU\dagger[/itex], where U is a unitary matrix and D is diagonal. Show that nay function of A defined by a power series,

[itex]f(A) = f_{0}I + f_{1}A + f_{2}A^{2} + ... + f_{n}A^{n} + ... [/itex]

can be expressed as [itex]f(A) = Uf(D)U\dagger[/itex]

The Attempt at a Solution



Not sure where to start. I know one can repeat a linear operator to get results such as [itex]A^{0} = 1, A^{1} = A, A^{2} = AA[/itex] but how do you show that for D? Are we simply doing the same? [itex]D^{0} = 1, D^{1} = D, D^{2} = DD [/itex]

So [itex]f(A) = U * f_{0}I + f_{1}D + f_{2}D^{2} + ... + f_{n}D^{n} + ... * U\dagger[/itex] ?

Thanks
 
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  • #2
Smazmbazm said:

Homework Statement



A diagonalisable square matrix A can be written [itex]A = UDU\dagger[/itex], where U is a unitary matrix and D is diagonal. Show that nay function of A defined by a power series,

[itex]f(A) = f_{0}I + f_{1}A + f_{2}A^{2} + ... + f_{n}A^{n} + ... [/itex]

can be expressed as [itex]f(A) = Uf(D)U\dagger[/itex]

The Attempt at a Solution



Not sure where to start. I know one can repeat a linear operator to get results such as [itex]A^{0} = 1, A^{1} = A, A^{2} = AA[/itex] but how do you show that for D? Are we simply doing the same? [itex]D^{0} = 1, D^{1} = D, D^{2} = DD [/itex]

So [itex]f(A) = U * f_{0}I + f_{1}D + f_{2}D^{2} + ... + f_{n}D^{n} + ... * U\dagger[/itex] ?

Thanks
That last line is messed up in some way. If you meant to start the right-hand side with ##U(\dots## and end it with ##\dots)U^\dagger##, then you have just written down the equality you're supposed to prove. But yes, that thing in the middle is ##f(D)##. So it can't be a bad idea to try to use that (the definition of ##f(D)##) to rewrite ##Uf(D)U^\dagger## in some way.
 
  • #3
Should it look like this,

[itex]f(A) = U*f_{0}I*U\dagger + U*f_{1}D*U\dagger + U*f_{2}D^{2}*U\dagger + ... + U*f_{n}D^{n}*U\dagger + ... [/itex] ?
 
  • #4
I assumed that you you were trying to write down the equality ##f(A)=Uf(D)U^\dagger##, with ##f(D)=1+f_0 D+\cdots+f_n D^n+\cdots##. You obviously can't assume that the former equality holds, since that's what you want to prove, but you can start
$$Uf(D)U^\dagger=U\left(1+f_0 D+\cdots+f_n D^n+\cdots\right)U^\dagger,$$ to see if you can end up with ##f(A)##. The right-hand side above can be written the way you just wrote it, but I wouldn't use ##*## for multiplication. It's standard to not use any symbol at all, and if you want to use one, it should be ##\circ##, since multiplication of linear operators is just composition of functions.
 
  • #5
Ok got it. Thanks for your help, Fredrik
 

Related to Show A = UDU(dagger) can be written as f(A) = Uf(D)U(dagger)

1. What does the equation "Show A = UDU(dagger) can be written as f(A) = Uf(D)U(dagger)" mean?

This equation means that a matrix A can be decomposed into the product of a unitary matrix U, a diagonal matrix D, and the conjugate transpose of U. It also shows that the same decomposition can be applied to a function of A, represented by f(A).

2. Why is this decomposition useful?

This decomposition is useful because it allows for easier manipulation and analysis of matrix operations. It can also be used to simplify calculations in areas such as quantum mechanics and signal processing.

3. How does this decomposition relate to eigenvalues and eigenvectors?

The diagonal matrix D in the decomposition contains the eigenvalues of A, while the columns of U are the eigenvectors of A. This shows the connection between the decomposition and eigenvalues/eigenvectors.

4. Is this decomposition unique?

No, this decomposition is not unique. There can be multiple ways to decompose a matrix A into the product of U, D, and U(dagger). However, the eigenvalues and eigenvectors will remain the same regardless of the specific decomposition.

5. Can this decomposition be applied to all matrices?

No, this decomposition can only be applied to square matrices. Additionally, for the decomposition to work, the matrix must be diagonalizable (have a full set of linearly independent eigenvectors).

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