Diagonalization and Unitary Matrices

In summary: Thank you for catching that!In summary, Maple incorrectly assumed that the number in the question is 1.
  • #1
Dewgale
98
9
\

Homework Statement


Let B = ##
\left[ \begin{array}{ccc} -1 & i & 1 \\ -i & 0 & 0 \\ 1 & 0 & 0 \end{array} \right]
##. Find a Unitary transformation to diagonalize B.

Homework Equations


N/A

The Attempt at a Solution


I have found both the Eigenvalues (0, 2, -1) and the Eigenvectors, which are ##<0,i,1>,\ \ <2,-i,1>,## and ##<-1,-i,1>##. Vectors 1 and 3 are both already normalized, but I normalized vector two to be ##<1,\frac{-i}{2},\frac{1}{2}>##.

They are now orthonormal, but the matrix formed from them,

$$U =
\left[ \begin{array}{ccc} 0 & 1 & -1\\ i & \frac{-i}{2} & -i \\ 1 & \frac{1}{2} & 1 \end{array} \right]$$

is not a unitary matrix. It will still succesfully diagonalize B, but I don't know what I've done wrong. Thank you!
 
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  • #2
Dewgale said:
\

Homework Statement


Let B = ##
\left[ \begin{array}{ccc} -1 & i & 1 \\ -i & 0 & 0 \\ 1 & 0 & 0 \end{array} \right]
##. Find a Unitary transformation to diagonalize B.

Homework Equations


N/A

The Attempt at a Solution


I have found both the Eigenvalues (0, 2, -1) and the Eigenvectors, which are ##<0,i,1>,\ \ <2,-i,1>,## and ##<-1,-i,1>##. Vectors 1 and 3 are both already normalized, but I normalized vector two to be ##<1,\frac{-i}{2},\frac{1}{2}>##.
The first and third eigenvectors aren't normalized, nor is the last one you show. For example, the magnitude of the first vector is ##\sqrt{2}##. The magnitude of a normalized vector is 1.
Dewgale said:
They are now orthonormal, but the matrix formed from them,

$$U =
\left[ \begin{array}{ccc} 0 & 1 & -1\\ i & \frac{-i}{2} & -i \\ 1 & \frac{1}{2} & 1 \end{array} \right]$$

is not a unitary matrix. It will still succesfully diagonalize B, but I don't know what I've done wrong. Thank you!
 
  • #3
Mark44 said:
The first and third eigenvectors aren't normalized, nor is the last one you show. For example, the magnitude of the first vector is ##\sqrt{2}##. The magnitude of a normalized vector is 1.
Oh shoot, yeah. My "norm" function in maple was spitting out ones, but I realized that I was using the wrong one.

It looks like two out of my three vectors are fine and normalized (## V_1 = <-1, -i, 1>, V_2 = <1, \frac{-i}{2}. \frac{1}{2}>##), but the third one, ##V_3 = < 0, -i, 1> ## has a magnitude of zero, so I don't know how to normalize it... I'm not sure where to go from here.
 
  • #4
Dewgale said:
Oh shoot, yeah. My "norm" function in maple was spitting out ones, but I realized that I was using the wrong one.

It looks like two out of my three vectors are fine and normalized (## V_1 = <-1, -i, 1>, V_2 = <1, \frac{-i}{2}. \frac{1}{2}>##),
No, they are not normalized. For a complex vector v, ##||v|| = \sqrt{v \cdot \bar{v}}##. ##||v_1|| = \sqrt{3}##, so it's clearly not normalized.
Dewgale said:
but the third one, ##V_3 = < 0, -i, 1> ## has a magnitude of zero
No. The only vector with a magnitude of zero is the zero vector. If Maple is giving you the wrong values, do them by hand.
Dewgale said:
, so I don't know how to normalize it... I'm not sure where to go from here.
 
  • #5
Dewgale said:
I have found the Eigenvalues (0, 2, -1) !
Are you sure 2 and -1 are eigenvalues?
 
  • #6
ehild said:
Are you sure 2 and -1 are eigenvalues?
That's a very good question.
 
  • #7
ehild said:
Are you sure 2 and -1 are eigenvalues?
Yep, about that I'm certain.

$$(Q - \lambda I) =
\left[ \begin{array}{ccc} 1-\lambda & i & 1\\ -i & -\lambda & 0 \\ 1 & 0 & -\lambda \end{array} \right]$$
Therefore ##det(Q - \lambda I) = 0## is
$$(1-\lambda)(-\lambda)(-\lambda) + \lambda + \lambda = 0$$
which is simplified to
$$ \lambda(\lambda - 2)(\lambda + 1) = 0$$
Therefore, ##\lambda = 0, 2, -1##.
 
  • #8
Mark44, thank you so much for your help! I'd forgotten that when finding the norm of a complex vector, you need the conjugate. I'm getting the right answer now.

Thank you!
 
  • #9
Dewgale said:
Yep, about that I'm certain.

$$(Q - \lambda I) =
\left[ \begin{array}{ccc} 1-\lambda & i & 1\\ -i & -\lambda & 0 \\ 1 & 0 & -\lambda \end{array} \right]$$
Therefore ##det(Q - \lambda I) = 0## is
$$(1-\lambda)(-\lambda)(-\lambda) + \lambda + \lambda = 0$$
which is simplified to
$$ \lambda(\lambda - 2)(\lambda + 1) = 0$$
Therefore, ##\lambda = 0, 2, -1##.
In your matrix, the term in column 1, row 1 is -1, not 1 as you assume here.
 
  • #10
Oh wow. That's a typo in my original post, the number in the question is 1.
 
  • #11
Mark44 said:
No, they are not normalized. For a complex vector v, ##||v|| = \sqrt{v \cdot \bar{v}}##. ##||v_1|| = \sqrt{3}##, so it's clearly not normalized.
No. The only vector with a magnitude of zero is the zero vector. If Maple is giving you the wrong values, do them by hand.

Maple gives the correct answer if the command is used correctly.
 

1. What is diagonalization of a matrix?

Diagonalization is a process of finding a diagonal matrix that is similar to a given square matrix. This means that the diagonal matrix has the same eigenvalues as the original matrix, but the eigenvectors are different. Diagonalization is often used to simplify calculations and solve certain systems of equations.

2. What is the importance of diagonalization?

Diagonalization is important in many areas of mathematics and science. It allows for easier computation of higher powers of a matrix, finding the inverse of a matrix, and solving systems of linear equations. In quantum mechanics, diagonalization of unitary matrices is used to find the energy levels and corresponding eigenstates of a system.

3. What are unitary matrices?

Unitary matrices are square matrices with complex entries that have the property of being unitary, meaning that their inverse is equal to their conjugate transpose. This property ensures that the columns of the matrix are orthonormal, which is useful in many applications, especially in quantum mechanics.

4. How are diagonalization and unitary matrices related?

Diagonalization and unitary matrices are related in that diagonalization is often used to find the eigenvalues and eigenvectors of a unitary matrix. This is because unitary matrices are easy to diagonalize, as they have a special form in which the columns are orthonormal. Also, the diagonal form of a unitary matrix is also unitary, making it useful for certain calculations.

5. Can any matrix be diagonalized?

No, not all matrices can be diagonalized. A matrix can only be diagonalized if it has a complete set of linearly independent eigenvectors. This means that the matrix must have a full set of distinct eigenvalues, and each eigenvalue must have a corresponding eigenvector. If a matrix does not meet these conditions, it cannot be diagonalized.

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