Condition on Leading principle minors of a symmetric Positive semidefinite(PSD) matri

In summary, the conversation discusses a symmetric positive semi-definite matrix, A, with certain conditions on its Leading Principle Minors. The question is whether A_{33}=A_{55}=0 can be determined from this information. The Interlacing Inequality is suggested as a possible tool to use. It is then confirmed that the answer is yes, as stated in Theorem 4.3.8 of "Matrix Analysis" by Horn and Johnson. The result also holds if A is a hermitian matrix. It is further noted that for a hermitian PSD matrix, if A_{KK}=0, then A_{MM}=0 for all M>K.
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Hi everyone,
This is related to my previous https://www.physicsforums.com/showthread.php?t=392069"

Let [tex] A=(a_{ij}) [/tex] be a symmetric (i.e., over reals) PSD matrix with the following conditions on Leading Principle Minors (determinant of the submatrix consisting of first i rows and i columns) [tex] A_{ii}[/tex]:

[tex] A_{11}\ge0,~ A_{22}=A_{44}= A_{66}=A_{77}=A_{88}=detA=0 [/tex]

Now the question is can I say (from the above information) that [tex] A_{33}=A_{55}=0 ?[/tex] From "Matrix Analysis" by Horn and Johnson, I guess the Interlacing Inequlity may be useful...but I don't know much about it. Any help, please.

As usual, will it still valid if I assume A to be hermitian (i.e., over complex) than being symmetric?Thanks
 
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  • #2


Oh, I got the answer. It is indeed yes.

The result follows directly from Theorem 4.3.8 (page-185) of the book I mentioned above. It is a consequence of the "Interlacing inequality" as I guessed. Below is a brief sketch:

By our assumption, [tex]A_{22}[/tex] must have an eigenvalue 0 and hence by the interlacing property, the least eigenvalue (which can not be negative as it is PSD) of [tex] A_{33}\mbox{~is} \le0[/tex]. Thus follows.

The result remains valid if A be hermitian.

More generally, we can say that for a hermitian PSD matrix [tex]A_{KK}=0~\Rightarrow~ A_{MM}=0~\forall M>K[/tex]
 
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Related to Condition on Leading principle minors of a symmetric Positive semidefinite(PSD) matri

1. What is a symmetric Positive semidefinite (PSD) matrix?

A symmetric Positive semidefinite (PSD) matrix is a square matrix where all its eigenvalues are non-negative. This means that the matrix is diagonalizable and all its diagonal entries are non-negative.

2. What is the leading principle minor of a PSD matrix?

The leading principle minor of a PSD matrix is the determinant of the top-left submatrix of a given size. For example, the 2x2 leading principle minor of a 3x3 PSD matrix is the determinant of the top-left 2x2 submatrix.

3. Why is it important to check the condition on leading principle minors of a PSD matrix?

Checking the condition on leading principle minors of a PSD matrix is important because it allows us to determine whether the given matrix is PSD or not. If all the leading principle minors are non-negative, then the matrix is PSD. Otherwise, it is not PSD.

4. What is the significance of the condition on leading principle minors in the context of PSD matrices?

The condition on leading principle minors is significant because it provides a necessary and sufficient condition for a matrix to be PSD. This means that if all the leading principle minors are non-negative, then the matrix is PSD, and if any of the leading principle minors is negative, then the matrix is not PSD.

5. How is the condition on leading principle minors used in practical applications?

The condition on leading principle minors is used in many practical applications, such as optimization problems, where PSD matrices often appear. It is also used in the analysis of systems and structures, such as in control theory and mechanics. Additionally, it is used in statistics and machine learning, where PSD matrices are commonly used for modeling covariance and correlation matrices.

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