Quick question about Hermitian operators

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    Hermitian Operators
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SUMMARY

In the discussion about Hermitian operators, it is established that while Hermitian operators have real eigenvalues, the converse is not true unless the operator is normal. A counterexample provided is the matrix \left(\begin{array}{cc} 0 & 1\\ 0 & 0 \end{array}\right). It is confirmed that a matrix is normal if it satisfies AA^*=A^*A, and such matrices can be diagonalized by a unitary transformation. Additionally, Hermitian matrices are always normal, ensuring their eigenvalues are real, while abnormal matrices may have real eigenvalues but lack orthogonal eigenvectors.

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If H is a Hermitian operator, then its eigenvalues are real. Is the converse true?
 
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No. Take as counterexample the matrix [tex]\left(\begin{array}{cc} 0 & 1\\ 0 & 0 \end{array}\right).[/tex]
However, if you know that the operator is normal (that is: if [itex]AA^*=A^*A[/itex]), then it is true that it is Hermitian iff the eigenvalues are real.
 
micromass said:
No. Take as counterexample the matrix [tex]\left(\begin{array}{cc} 0 & 1\\ 0 & 0 \end{array}\right).[/tex]
However, if you know that the operator is normal (that is: if [itex]AA^*=A^*A[/itex]), then it is true that it is Hermitian iff the eigenvalues are real.

Good counterexample!

A few more useful rules: a matrix ##M## has "good" eigenvectors if and only if it is normal. "Good" eigenvectors means it is possible to choose eigenvectors such that they form an orthonormal basis for whatever vector space ##M## acts on.

Equivalently, a matrix is normal if and only if it can be diagonalized by a unitary transformation:
##
M = U D U^{-1}
##
for some unitary matrix ##U## and diagonal matrix ##D## whose diagonal elements are eigenvalues of ##M##.

Hermitian matrices are always normal, and their eigenvalues are always real. But if ##M## is abnormal, it might have real eigenvalues and "bad" eigenvectors which are not orthogonal. Here's an example:

##
\left[\begin{array}{cc}
1 & 1 \\
0 & 2 \\
\end{array}\right]
##

This thing has eigenvalues 1 and 2, but its eigenvectors

##
\left[\begin{array}{c}
1 \\ 0 \\
\end{array}\right]
\quad
\left[\begin{array}{c}
1 \\ 1 \\
\end{array}\right]
##

are not orthogonal to each other.

The example micromass gave is even more badly-behaved: it is defective, which means it doesn't have enough linearly-independent eigenvectors to span the vector space. So we can't even form a "bad" basis using its eigenvectors.
 
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