Eigenvector orthogonality and unitary operator diagonalization

In summary: in summary, the homework statement is to find the eigenvalues and eigenvectors of an array, using the eulers equation.
  • #1
Gary Roach
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0

Homework Statement


For reference: Problem 1.8.5 parts (3) , R. Shankar, Principles of Quantum Mechanics.
Given array [tex] \Omega [/tex], compute the eigenvalues ([itex] e^i^\theta [/itex] and [itex] e^-^i^\theta [/itex]). Then (3) compute the eigenvectors and show that they are orthogonal.

Homework Equations


Eulers equation needed:
[tex] e^\pm^i^\theta = \cos\theta \pm i\sin\theta [/tex]

The matrix;
[tex] \Omega = \left |\begin{array}{cc}\cos\theta& \sin\theta\\-\sin\theta& \sin\theta\end{array} \right| [/tex]



The Attempt at a Solution


Computer eigenvectors (Some QM ket notation used)
[tex] |\omega = e^i^\theta > \Rightarrow \left|\begin{array}{cc}\cos\theta - (\cos\theta + i\sin\theta)& \sin\theta \\-\sin\theta& \cos\theta - (\cos\theta +i\sin\theta) \end{array} \right | [/tex]

[tex] |\omega = e^i^\theta > \Rightarrow \sin\theta\left|\begin{array}{cc}-i& 1\\-1& -i \end{array}\right | [/tex]
Since the array must equal the zero array, [tex]\sin\theta [/tex] can be eliminated and:
[tex] -ix_1 + x_2 = 0 \\
-x_1 + -ix_2 = 0 \\
\Rightarrow x_1 = -ix_2 [/tex]

So the eigenvector should be:
[tex] |\omega = e^i^\theta > = \left|\begin{array}{cc}-i\\1\end{array}\right| \\ letting& x_2=1[/tex]

Similarly:
[tex] |\omega = e^-^i^\theta > \Rightarrow \sin\theta\left|\begin{array}{cc}i& 1\\-1& i \end{array}\right | [/tex]
[tex] ix_1 + x_2 = 0 \\
-x_1 + ix_2 = 0 \\
\Rightarrow x_1 = ix_2 [/tex]
So the eigenvector should be:
[tex] |\omega = e^-^i^\theta > = \left|\begin{array}{cc}i\\1\end{array}\right| [/tex]

I think I have all of this right but unfortunately these two vectors are supposed to be orthogonal implying that their inner product sould be zero. And:
[tex] \left|\begin{array}{cc}-i\\1\end{array}\right| \cdot \left|\begin{array}{cc}i\\1\end{array}\right| = 1+1 =2 \neq 0[/tex] ??

It's probably a stupid error but I can't find it. Any help will be sincerely appreciated. I hope this get cleaned up in the final version. The preview feature won't show corrections.

Gary R.
 
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  • #2
The inner product here is <a,b>=conjugate(a^(T))b. You forgot the complex conjugate on the first vector.
 
  • #3
Oops. There seems to be a hole here in my knowledge. Oh well, back to the books. Thanks for the prompt reply.

Gary R.
 

Related to Eigenvector orthogonality and unitary operator diagonalization

1. What are eigenvectors and why are they important in linear algebra?

Eigenvectors are special vectors that remain in the same direction after being transformed by a linear operator. They are important in linear algebra because they provide a basis for understanding the behavior of linear transformations and can be used to simplify and solve complex problems.

2. Can eigenvectors be orthogonal to each other?

Yes, eigenvectors can be orthogonal to each other. Orthogonal eigenvectors have a dot product of zero, meaning they are perpendicular to each other. This property is often used in diagonalization, where eigenvectors are used to create an orthogonal matrix that simplifies the transformation of a matrix into a diagonal form.

3. What is the relationship between eigenvectors and unitary operators?

Unitary operators are linear operators that preserve the length and angle of vectors. Eigenvectors of a unitary operator are also unitary vectors, meaning they are normalized and orthogonal to each other. This property makes unitary operators useful for diagonalization, as they can transform a matrix into a diagonal form without changing the underlying structure of the problem.

4. How is unitary operator diagonalization related to spectral decomposition?

Unitary operator diagonalization is a special case of spectral decomposition, where a matrix is decomposed into a diagonal matrix and a unitary matrix. The diagonal matrix contains the eigenvalues of the original matrix, while the unitary matrix is composed of the corresponding eigenvectors. This process is used to simplify complex problems and make them easier to solve.

5. Can any matrix be diagonalized by a unitary operator?

Yes, any square matrix can be diagonalized by a unitary operator. This is known as the spectral theorem, which states that any normal matrix (a matrix that commutes with its conjugate transpose) can be diagonalized by a unitary operator. However, not all matrices can be diagonalized by a unitary operator, as there are some conditions that need to be met.

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