Check the spectral theorem for this matrix

In summary, the conversation discusses three projection operators, each with its own unique properties and eigenvalues. The speaker is struggling to understand how to interpret these properties in a discrete situation where there are only three eigenvalues. The definition of E in this context is a projection operator valued function, which is a step function with a spike at each eigenvalue. The derivative of E with respect to lambda has a delta function spike at each eigenvalue, allowing for the discrete eigenvalues to be determined through integration.
  • #1
LCSphysicist
646
162
Homework Statement
.
Relevant Equations
.
1615235843259.png

I found three projection operators
$$P_{1}=
\begin{pmatrix}
1/2 & & \\
& -\sqrt{2}/2 & \\
& & 1/2
\end{pmatrix}$$
$$P_{2}=
\begin{pmatrix}
1/2 & & \\
& \sqrt{2}/2 & \\
& & 1/2
\end{pmatrix}$$
$$P_{3}=
\begin{pmatrix}
-1/\sqrt{2} & & \\
& & \\
& & 1/\sqrt{2}
\end{pmatrix}$$

From this five properties
1615235980298.png
, i am having trouble to prove the ii, iii, and iv. I mean, i could understand this conditions in the continuous situation, in which we can make the "eigenvalue tends to" something. But how to interpret it when we are dealing with discrete conditions? In which we have really just three eigenvalues.
 

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  • #2
What is the definition of E in those statements?
 
  • #3
##E(\lambda)## is an projection (operator) valued function. In the case of discrete eigenvalues it is a step function which has a step at each eigenvalue . ##dE(\lambda)/d\lambda## has a ##\delta## function spike at each eigenvalue, which gives you the discrete eigenvalue when you integrate.
 

FAQ: Check the spectral theorem for this matrix

What is the spectral theorem for a matrix?

The spectral theorem for a matrix is a mathematical theorem that states that any square matrix can be diagonalized by a unitary matrix, and the diagonal elements are the eigenvalues of the original matrix.

How do I check the spectral theorem for a matrix?

To check the spectral theorem for a matrix, you need to find the eigenvalues and eigenvectors of the matrix. Then, you can use the eigenvalues to construct a diagonal matrix and the eigenvectors to construct a unitary matrix. Multiplying these two matrices should result in the original matrix, confirming the spectral theorem.

What is the significance of the spectral theorem in linear algebra?

The spectral theorem is significant in linear algebra because it allows us to simplify and understand complex matrices by breaking them down into their eigenvalues and eigenvectors. This can be useful in solving systems of linear equations, computing matrix powers, and understanding the behavior of linear transformations.

Can the spectral theorem be applied to non-square matrices?

No, the spectral theorem only applies to square matrices. Non-square matrices do not have eigenvalues or eigenvectors, which are necessary for the spectral theorem to hold.

Why is the spectral theorem important in quantum mechanics?

The spectral theorem is important in quantum mechanics because it allows us to describe the properties of quantum systems in terms of their eigenvalues and eigenvectors. This helps us understand the behavior of quantum particles and make predictions about their measurements and interactions.

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