Hermitian Operators and Projectors in Linear Algebra

In summary, the conversation discusses the properties of a matrix, specifically its symmetry and being a projector. The first matrix is not symmetric and is not a Hermitian operator, while the second one is. The conversation also touches on the concept of basis transformation and conservation of symmetry. The term "projector" is used to refer to a linear transformation that is a projection with orthogonal null space and range. The distinction of this term may vary in different languages.
  • #1
LagrangeEuler
717
20
Matrix
[tex]
\left[
\begin{array}{rr}
1 & 1 \\
0& 0 \\
\end{array} \right][/tex]
is not symmetric. When we find eigenvalues of that matrix we get ##\lambda_1=1##, ##\lambda_2=0##, or we get matrix
[tex]
\left[
\begin{array}{rr}
1 & 0 \\
0& 0 \\
\end{array} \right][/tex].
First matrix is not hermitian, whereas second one it is. How it is possible that some operator is hermitian in one basis, and is not in the other one. Second matrix is also a projector, and the first one it is not. How that is possible.
 
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  • #2
Have you considered how a basis transformation has to look like, in order to conserve symmetry? Does yours look like one? And what do you mean by a projector? The first matrix is also a projection, i.e. surjective, just not at the same angle.
 
  • #3
fresh_42 said:
And what do you mean by a projector?.

In English linear algebra references, a projector (on an inner product space) typically is a linear transformation that is a projection, and that has orthogonal null space and range, i.e., idempotent and Hermitian. I am not sure if other languages make this distinction.
 
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1. What is diagonalization of matrices?

Diagonalization of matrices is a process in linear algebra where a square matrix is transformed into a diagonal matrix by finding a new set of basis vectors that are aligned with the eigenvectors of the original matrix.

2. Why is diagonalization of matrices useful?

Diagonalization of matrices is useful because it simplifies calculations and makes it easier to solve systems of linear equations. It also helps in finding the inverse of a matrix and in solving differential equations.

3. How do you diagonalize a matrix?

To diagonalize a matrix, you need to find the eigenvalues and corresponding eigenvectors of the matrix. Then, these eigenvectors are used to form a diagonal matrix, with the eigenvalues on the diagonal and zeros everywhere else.

4. Can all matrices be diagonalized?

No, not all matrices can be diagonalized. A matrix can only be diagonalized if it has n linearly independent eigenvectors, where n is the size of the matrix. If there are not enough linearly independent eigenvectors, the matrix cannot be diagonalized.

5. What is the significance of the eigenvalues in diagonalization of matrices?

The eigenvalues in diagonalization of matrices represent the scaling factor for each eigenvector. They also indicate the stability of a system and can be used to determine the behavior of a system over time.

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