Diagonalizing a Complex Matrix: Unitary Matrix Approach

In summary, to solve the question of finding the unitary matrix that diagonalizes the given matrix, one must first understand the concept of unitary matrices and diagonalizable matrices. Then, the eigenvectors and eigenvalues of the matrix must be found, and an orthonormal set of eigenvectors must be created. Finally, these eigenvectors can be used to form a unitary matrix that diagonalizes the given matrix.
  • #1
tas3113
31
0
I am going over old practice exams and came across this question:

Find the unitary matrix which diagonalizes the matrix

...i 1 0
A = (-1 i 0 )
... 0 0 -i


First off, can someone explain to me about unitary matrices and get me started on this question? I do not know where to begin solving it. Thank you!
 
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  • #2
A unitary matrix is a matrix for which [itex]U^{\dagger}U^{-1}=U^{-1}U^\dagger=I[/itex]. So [itex]U^\dagger=U^{-1}[/itex]. A matrix B is diagonalizable if there exists a matrix C such that [itex]C^{-1}BC[/itex] is diagonal.
 
  • #3
so i found the conjugate transpose of the matrix and got

(-i -1 0)
(1 -i 0)
(0 0 i)

is that a start?
 
  • #4
No, the idea is that you find [itex]U^\dagger A U[/itex]. A need not be unitary. The first step is to find the eigenvalues of A and their respective eigenvectors. You can however use [itex]A^\dagger[/itex] to check if A is unitarily diagonalizable. A matrix A is unitarily diagonalizable if and only if [itex]A^\dagger A=A A^\dagger[/itex]. Perhaps it's nice to check this for A, although of course A is unitarily diagonalizable.

After you have the eigenvectors check if they form an orthonormal set. If they don't you have to make an orthonormal set by the Gram-Schmidt process. Now make a matrix with its columns being the eigenvectors.
 
Last edited:
  • #5
so i came up with the equation:
-h(h^2+1)+h^2 i

so the eigenvalues are 0, -i, and something else but i don't know how to find that. 0 = ih^2

the eigenvectors are (1,- i,0) , (0,0,1), and something..
 
  • #6
That equation doesn't look right to me. I got [itex](-i-\lambda)[(i-\lambda)(i-\lambda)+1]=0[/itex]
 
  • #7
so its
[tex]
\left|\begin{array}{ccc}i-\lambda & 1 & 0 \\ -1 & i-\lambda & 0 \\ 0 & 0 & -i-\lambda\end{array}\right|
[/tex]

so then
[itex]
[(i-\lambda)(i-\lambda)(-i-\lambda)]-[(-1)(1)(-i-\lambda)]=0
[/itex]
oo.. i forgot the lambda for the last (-i) before.. ok so
[itex]
(i-\lambda)(i-\lambda)(-i-\lambda)-(i+\lambda)=0
[/itex]
and so
[itex]
(-i-\lambda)[(i-\lambda)(i-\lambda)+1]=0
[/itex]
is right

so eigenvalues are 0, -i, 2i
with eigenvectors: (1,- i,0), (0,0,1), (1, i,0)

ok... so as for orthonormal set:
(1,- i,0), (0,0,1) are perpendicular
(0,0,1), (1, i,0) are perpendicular

then? lost from there
 
  • #8
Well this set of eigenvectors is orthogonal and we want an orthonormal set. So you need to normalize them. After that you can simply form a matrix with the eigenvectors as columns. Then test if it's unitary and diagonalize the matrix A.

A tip when it comes to calculating determinants. You don't always have to start from the top left and work to the top right when you write a 3x3 determinant as the sum of 2x2 determinants. Like in this case the fastest way is:

[tex]
\left|\begin{array}{ccc}i-\lambda & 1 & 0 \\ -1 & i-\lambda & 0 \\ 0 & 0 & -i-\lambda\end{array}\right|=(-i-\lambda)\left|\begin{array}{cc}i-\lambda & 1 \\ -1 & i-\lambda & \end{array}\right|
[/tex]

Start at the bottom right, because that row/column has the most zeros.
 
  • #9
aren't they already normalized?
u1 = (1,- i,0)
u2 = (0,0,1) - (0+0+0)/... doesn't matter
u3 = (1, i,0) - (0+0+0)/... doesn't matter

so then i would divide by ||u||
u1: squareroot(1^2+(-i)^2+0) = 0 --well then itd be undef if u divide by 0
u2: 1
u3: 0
 
  • #10
A normalized vector has length 1. So [itex]||u_1||=\sqrt{(u_1,u_1)}=\sqrt{2}[/itex]. Normalizing u1 gives [itex]1/\sqrt{2}(1,-i,0)[/itex]. It works the same for the others.
 
  • #11
ok. i ended up with the vectors
[itex]
(1/\sqrt{2}, -i/\sqrt{2}, 0), (0, 0, 1), (1/\sqrt{2}, i/\sqrt{2}, 0)
[/itex]

which gives me the matrix
[tex]
\left|\begin{array}{ccc}1/\sqrt{2} & 0 & 1/\sqrt{2} \\ -i/\sqrt{2} & 0 & i/\sqrt{2} \\ 0 & 1 & 0\end{array}\right|
[/tex]

and the conjugate transpose is
[tex]
\left|\begin{array}{ccc}1/\sqrt{2} & i/\sqrt{2} & 0 \\ 0 & 0 & 1 \\ 1/\sqrt{2} & -i/\sqrt{2} & 0\end{array}\right|
[/tex]

then
[tex]
\left|\begin{array}{ccc}1/\sqrt{2} & 0 & 1/\sqrt{2} \\ -i/\sqrt{2} & 0 & i/\sqrt{2} \\ 0 & 1 & 0\end{array}\right|
[/tex] * [tex]
\left|\begin{array}{ccc}1/\sqrt{2} & i/\sqrt{2} & 0 \\ 0 & 0 & 1 \\ 1/\sqrt{2} & -i/\sqrt{2} & 0\end{array}\right|
[/tex] = [tex]
\left|\begin{array}{ccc}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{array}\right|
[/tex]
which is already diagonalized

so then the unitary matrix/answer is:
[tex]

\left|\begin{array}{ccc}1/\sqrt{2} & i/\sqrt{2} & 0 \\ 0 & 0 & 1 \\ 1/\sqrt{2} & -i/\sqrt{2} & 0\end{array}\right|

[/tex]
?
 
  • #12
Yep that is correct. You've shown that [itex]U^\dagger U=I[/itex]. Therefore U is unitary. You can test if U indeed diagonalizes A by calculating [itex]U^\dagger A U[/itex].
 
  • #13
ok. thank you!
 

What is a unitary matrix?

A unitary matrix is a square matrix whose conjugate transpose is equal to its inverse. In other words, the matrix multiplied by its complex conjugate is equal to the identity matrix.

What is the significance of a unitary matrix in linear algebra?

A unitary matrix plays a crucial role in linear algebra as it preserves the length and angles of vectors. This property makes it useful in applications such as quantum mechanics, signal processing, and data compression.

How do you determine if a matrix is unitary?

To determine if a matrix is unitary, you can check if its conjugate transpose is equal to its inverse. You can also check if the columns (or rows) of the matrix form an orthonormal basis.

Can a non-square matrix be unitary?

No, a non-square matrix cannot be unitary. A unitary matrix is defined as a square matrix, and the conjugate transpose of a non-square matrix cannot be multiplied by the original matrix to give the identity matrix.

What are some applications of unitary matrices?

Unitary matrices have various applications in mathematics, physics, and engineering. Some examples include quantum computing, digital signal processing, and image compression. They are also used in solving systems of linear equations and in finding eigenvalues and eigenvectors of a matrix.

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