Hermitian, positive definite matrices

In summary: A? That is the only way I can think of this matrix is related to A. The matrix with all zeros except the upper left corner is ##\Delta_k## is essentially A, when k = m. Should I visualizing the submatrices ##\Delta_k## a matrix of zeros except with the upper left corner as... part of A? That is the only way I can think of this matrix is related to A.In summary, the conversation discusses how to prove that a specific submatrix, ##\Delta_k##, is positive definite if the original matrix, A, is positive definite. It is mentioned that the definition of positive-definiteness can be used to prove this, and that there may be a relation
  • #1
pyroknife
613
4

Homework Statement


I am trying to prove the following:
if ##A\in C^{m\ \text{x}\ m}## is hermitian with positive definite eigenvalues, then A is positive definite. This was a fairly easy proof. The next part wants me to prove if A is positive definite, then ##\Delta_k##=\begin{bmatrix} a_{11} & \cdots & a_{1k} \\ \vdots & \ddots & \vdots\\ a_{k1} & \cdots & a_{kk} \end{bmatrix}

is also positive definite. k = 1...m

Homework Equations

The Attempt at a Solution


Since the first part I already proved if some matrix is hermitian with positive eigen values then that matrix is positive definite. I basically need to prove the ##\Delta_k## is hermitian with positive eigenvalues, therefore its positive definite.

It is obvious that ##\Delta_k## is hermitian since it is simply just a sub matrix of A, but how do I go about proving that this matrix also has positive eigenvalues? Any hints? Seems like it should be obvious, but I can't think of how to prove it.
 
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  • #2
pyroknife said:

Homework Statement


I am trying to prove the following:
if ##A\in C^{m\ \text{x}\ m}## is hermitian with positive definite eigenvalues, then A is positive definite. This was a fairly easy proof. The next part wants me to prove if A is positive definite, then ##\Delta_k##=\begin{bmatrix} a_{11} & \cdots & a_{1k} \\ \vdots & \ddots & \vdots\\ a_{k1} & \cdots & a_{kk} \end{bmatrix}

is also positive definite. k = 1...m

Homework Equations

The Attempt at a Solution


Since the first part I already proved if some matrix is hermitian with positive eigen values then that matrix is positive definite. I basically need to prove the ##\Delta_k## is hermitian with positive eigenvalues, therefore its positive definite.

It is obvious that ##\Delta_k## is hermitian since it is simply just a sub matrix of A, but how do I go about proving that this matrix also has positive eigenvalues? Any hints? Seems like it should be obvious, but I can't think of how to prove it.
How does ##\Delta_k## act on vectors outside of the k-dimensional subspace spanned by the first k unit vectors? (If you use it as the upper left block of an mxm matrix which is otherwise zero)
 
  • #3
Orodruin said:
How does ##\Delta_k## act on vectors outside of the k-dimensional subspace spanned by the first k unit vectors? (If you use it as the upper left block of an mxm matrix which is otherwise zero)
Hmm. Does this involve eigenvalues?

Or are you suggesting that I check ##x^T \Delta_k x##
 
  • #4
pyroknife said:
Hmm. Does this involve eigenvalues?

Or are you suggesting that I check ##x^T \Delta_k x##

Well that is exactly the DEFINITION of positive-definite.
 
  • #5
Ray Vickson said:
Well that is exactly the DEFINITION of positive-definite.
Yes. I was thinking of just proving the eigenvalues are positive, based on the first part of the problem. If I think ##x^T \Delta_k x##, this would not involve eigenvalues in any way, right?
 
  • #6
pyroknife said:
Yes. I was thinking of just proving the eigenvalues are positive, based on the first part of the problem. If I think ##x^T \Delta_k x##, this would not involve eigenvalues in any way, right?

Well, the eigenvalues of a ##k \times k## submatrix may be different from any of those of the original matrix, so I cannot see how using eigenvalues will help; you no longer know they are all > 0!
 
  • #7
Ray Vickson said:
Well, the eigenvalues of a ##k \times k## submatrix may be different from any of those of the original matrix, so I cannot see how using eigenvalues will help; you no longer know they are all > 0!
Hmm. I see. I thought there was some sort of relation between the eigen values of the original matrix and a sub-top-left matrix. I guess not.
If I do ##x^T \Delta_k x##, is there a simple way to show this for a generic ##\Delta_k## matrix? I can write out the vector-matrix multiplication & summing them up to show its >0, but that seems tedious. I am wondering if there's a much simpler way to show this.
 
  • #8
pyroknife said:
Hmm. I see. I thought there was some sort of relation between the eigen values of the original matrix and a sub-top-left matrix. I guess not.
If I do ##x^T \Delta_k x##, is there a simple way to show this for a generic ##\Delta_k## matrix? I can write out the vector-matrix multiplication & summing them up to show its >0, but that seems tedious. I am wondering if there's a much simpler way to show this.

There might be such eia relation, but I have never heard of one, and I doubt it would be easy to find even if it exists.

I honestly do not understand your difficulty; it all follows in one line and with almost zero work from the definition of positive-definiteness of the original matrix ##A##.
 
  • #9
Ray Vickson said:
There might be such eia relation, but I have never heard of one, and I doubt it would be easy to find even if it exists.

I honestly do not understand your difficulty; it all follows in one line and with almost zero work from the definition of positive-definiteness of the original matrix ##A##.
Hmm, I am not seeing this.
Since A is positive definite, that means:
##x^T A x > 0## for all ##x\neq 0##
But I am not sure how this would relate to ##\Delta_k##. There's something I'm not visualizing. Are you looking at this from an element by element multiplication perspective?
 
  • #10
@pyroknife Have you considered Orodruins hint?

Think about the mxm matrix which is all zeros except the upper left corner is Δk ... how is this matrix related to A?
 
  • #11
Nathanael said:
@pyroknife Have you considered Orodruins hint?

Think about the mxm matrix which is all zeros except the upper left corner is Δk ... how is this matrix related to A?
The matrix with all zeros except the upper left corner is ##\Delta_k## is essentially A, when k = m. Should I visualizing the submatrices ##\Delta_k## a matrix of zeros except with the upper left corner as ##\Delta_k##?
 
  • #12
Let me use ##\Delta_k^{mxm}## to represent the mxm matrix of all zeros except the upper left corner is Δk (just so I can reference it easily).
pyroknife said:
The matrix with all zeros except the upper left corner is ##\Delta_k## is essentially A, when k = m.
Ok, true. But in general, when k ≠ m, how do you get from A to ##\Delta_k^{mxm}##?
What I mean specifically is, what matrix takes you from A to ##\Delta_k^{mxm}##?
 
  • #13
Nathanael said:
Let me use ##\Delta_k^{mxm}## to represent the mxm matrix of all zeros except the upper left corner is Δk (just so I can reference it easily).

Ok, true. But in general, when k ≠ m, how do you get from A to ##\Delta_k^{mxm}##?
What I mean specifically is, what matrix takes you from A to ##\Delta_k^{mxm}##?
Oh. hmm, let's see. if k = m, then the matrix would just be identity.
If k < m, then this gets a little complicated. Let me see if I can get a relationship for this matrix. This matrix basically needs to introduce m-k columns&rows of zeros at the end of ##\Delta_k^{mxm}##
 
  • #14
pyroknife said:
The matrix with all zeros except the upper left corner is ##\Delta_k## is essentially A, when k = m. Should I visualizing the submatrices ##\Delta_k## a matrix of zeros except with the upper left corner as ##\Delta_k##?

You want to know if the quadratic form
[tex] Q_k(x_1, x_2, \ldots, x_k) = (x_1, x_2, \ldots, x_k) \pmatrix{a_{11} & a_{12}& \cdots & a_{ik} \\
a_{21} & a_{22} & \cdots & a_{2k}\\
\vdots & \vdots & \ddots & \vdots \\
a_{k1} & a_{k2} & \cdots & a_{kk} }
\pmatrix{x_1 \\ x_2 \\ \vdots \\x_k}
[/tex]
is always positive for ##(x_1, x_2, \ldots, x_k) \neq (0,0,\ldots, 0)##.
 
  • #15
Ray Vickson said:
You want to know if the quadratic form
[tex] Q_k(x_1, x_2, \ldots, x_k) = (x_1, x_2, \ldots, x_k) \pmatrix{a_{11} & a_{12}& \cdots & a_{ik} \\
a_{21} & a_{22} & \cdots & a_{2k}\\
\vdots & \vdots & \ddots & \vdots \\
a_{k1} & a_{k2} & \cdots & a_{kk} }
\pmatrix{x_1 \\ x_2 \\ \vdots \\x_k}
[/tex]
is always positive for ##(x_1, x_2, \ldots, x_k) \neq (0,0,\ldots, 0)##.
Yes exactly. But it seems to show that the quadratic form is > 0, I'd need to carry out all the matrix multiplications and verify this is > 0?
 
  • #16
pyroknife said:
Yes exactly. But it seems to show that the quadratic form is > 0, I'd need to carry out all the matrix multiplications and verify this is > 0?
Ray Vickson said:
You want to know if the quadratic form
[tex] Q_k(x_1, x_2, \ldots, x_k) = (x_1, x_2, \ldots, x_k) \pmatrix{a_{11} & a_{12}& \cdots & a_{ik} \\
a_{21} & a_{22} & \cdots & a_{2k}\\
\vdots & \vdots & \ddots & \vdots \\
a_{k1} & a_{k2} & \cdots & a_{kk} }
\pmatrix{x_1 \\ x_2 \\ \vdots \\x_k}
[/tex]
is always positive for ##(x_1, x_2, \ldots, x_k) \neq (0,0,\ldots, 0)##.

I have already showed that A is positive definite (not in this thread, but let's just assume I showed you guys).
Which means

##Q_m(x_1, x_2, \ldots, x_m) = (x_1, x_2, \ldots, x_m) \pmatrix{a_{11} & a_{12}& \cdots & a_{1m} \\
a_{21} & a_{22} & \cdots & a_{2m}\\
\vdots & \vdots & \ddots & \vdots \\
a_{k1} & a_{k2} & \cdots & a_{mm} }
\pmatrix{x_1 \\ x_2 \\ \vdots \\x_m} > 0##

Now I need to show that if I take out (m-k) elements from ##x## and m-k rows&columns from A, then this still yields positive definite matrix.
 
  • #17
pyroknife said:
Oh. hmm, let's see. if k = m, then the matrix would just be identity.
If k < m, then this gets a little complicated. Let me see if I can get a relationship for this matrix. This matrix basically needs to introduce m-k columns&rows of zeros at the end of ##\Delta_k^{mxm}##
You shouldn't have to go too far from the identity matrix to find it; maybe put some zeros in the right spots?

Let me try to hint at the proof in another way:
If ##\vec x ^TA\vec x >0## for all ##\vec x## then in particular it must be true for all ##\vec x## of the form ##\vec x^T = [x_1, ... , x_k, 0, ..., 0]## right?
(Sorry I don't know how to write a column vector in latex.)
 
  • #18
Nathanael said:
You shouldn't have to go to far from the identity matrix to find it; maybe put some zeros in the right spots?

Let me try to hint at the proof in another way:
If ##\vec x ^TA\vec x >0## for all ##\vec x## then in particular it must be true for all ##\vec x## of the form ##\vec x^T = [x_1, ... , x_k, 0, ..., 0]## right?
(Sorry I don't know how to write a column vector in latex.)
Yes that is true. So we are one step closer, but the next part, if we add the (m-k) columns and rows of zeros, I am not sure why that would still yield greater than zero.
Edit: Never mind, I undertand that if we modify A and add the (m-k) columns and rows of zeros, it would still yield a positive definite matrix. ##x^T \Delta_k^{mxm} x = x_k^T \Delta_k x_k## is that correct?

where ##x_k = \begin{bmatrix}
x_1 \\
x_2 \\
\vdots\\
x_k
\end{bmatrix}
##
 
  • #19
I think the above proves it, but I'm still thinking about the matrix, when multiplied to A, yields zeros in the last (m-k) columns&rows. I was thinking it should just be a slight modification of the identity matrix, but I can't get it to work.
 
  • #20
Are you convinced that ##\vec x^T\Delta_k^{mxm}\vec x>0## for all ##\vec x \in C^{m}## implies that ##\vec v^T\Delta_k\vec v>0## for all ##\vec v \in C^{k}## ?
(This was the one part of the proof that I'm not sure of the best way to prove, but it seems clear enough.)

If so, then the goal becomes to show ##\vec x^T\Delta_k^{mxm}\vec x>0##. This follows nicely from the positive-definiteness of A if you think about the matrix which takes you from A to ##\Delta_k^{mxm}##

Now what is that matrix? I'm not sure how to hint at it without just saying it, but like I said it's similar to the identity matrix except with some extra zeros...
 
  • #21
pyroknife said:
I think the above proves it, but I'm still thinking about the matrix, when multiplied to A, yields zeros in the last (m-k) columns&rows. I was thinking it should just be a slight modification of the identity matrix, but I can't get it to work.
[tex] Q_k(x_1, x_2, \ldots, x_k) = Q_m(x_1, x_2, \ldots, x_k, 0, 0 , \ldots 0) [/tex]
No need to agonize over matrices; just write things out as ##Q_m = \sum_{i=1}^m \sum_{j=1}^m a_{ij} x_i x_j## and ##Q_k = \sum_{i=1}^k \sum_{j=1}^k a_{ij} x_i x_j##.
 
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  • #22
Nathanael said:
Are you convinced that ##\vec x^T\Delta_k^{mxm}\vec x>0## for all ##\vec x \in C^{m}## implies that ##\vec v^T\Delta_k\vec v>0## for all ##\vec v \in C^{k}## ?
(This was the one part of the proof that I'm not sure of the best way to prove, but it seems clear enough.)

If so, then the goal becomes to show ##\vec x^T\Delta_k^{mxm}\vec x>0##. This follows nicely from the positive-definiteness of A if you think about the matrix which takes you from A to ##\Delta_k^{mxm}##

Now what is that matrix? I'm not sure how to hint at it without just saying it, but like I said it's similar to the identity matrix except with some extra zeros...
Hold on, isn't
##x^T \Delta_k^{mxm} x = x_k^T \Delta_k x_k## ?

Because if so, then we have already proved ##\Delta_k ## is positive definite
 
  • #23
pyroknife said:
Hold on, isn't
##x^T \Delta_k^{mxm} x = x_k^T \Delta_k x_k## ?
Multiplication can only be defined on one side of that equation.
(x either has k components or m components; either way it can't multiply both ##\Delta_k^{mxm}## and ## \Delta_k##)

Edit:
I didn't notice you put a subscript k on x on the right side. So then you're saying it's equal for any arbitrary x and xk? Surely that won't be true.

Also I didn't notice it from Ray's view before; my proof is slightly different. Maybe it would be better with his approach.
 
  • #24
Ray Vickson said:
[tex] Q_k(x_1, x_2, \ldots, x_k) = Q_m(x_1, x_2, \ldots, x_k, 0, 0 , \ldots 0) [/tex]
No need to agonize over matrices; just write things out as ##Q_m = \sum_{i=1}^m \sum_{j=1}^m a_{ij} x_i x_j## and ##Q_k = \sum_{i=1}^k \sum_{j=1}^k a_{ij} x_i x_j##.

##Q_m = \sum_{i=1}^m \sum_{j=1}^m a_{ij} x_i x_j## and ##Q_k = \sum_{i=1}^k \sum_{j=1}^k a_{ij} x_i x_j##
I see this part.

Is the following true?
##Q_k = Q_m - \sum_{i=k+1}^m \sum_{j=k+1}^m a_{ij} x_i x_j##
If so, then since ##Q_m > 0##, how do we know this second term is not ##=> Q_m##?
 
  • #25
Nathanael said:
Multiplication can only be defined on one side of that equation.
(x either has k components or m components; either way it can't multiply both ##\Delta_k^{mxm}## and ## \Delta_k##)

Edit:
I didn't notice you put a subscript k on x on the right side. So then you're saying it's equal for any arbitrary x and xk? Surely that won't be true.

Also I didn't notice it from Ray's view before; my proof is slightly different. Maybe it would be better with his approach.
Yes, I was saying it's equal for ##x_k## such that ##x_k## is the first ##k## elements of an arbitrary ##x##. For some reason I thought this was true?

I was thinking the matrix we were talking about is just the ##mxm## identity matrix except with zeros for the last (m-k) columns and rows, but this doesn't seem to work.
 
  • #26
pyroknife said:
##Q_m = \sum_{i=1}^m \sum_{j=1}^m a_{ij} x_i x_j## and ##Q_k = \sum_{i=1}^k \sum_{j=1}^k a_{ij} x_i x_j##
I see this part.

Is the following true?
##Q_k = Q_m - \sum_{i=k+1}^m \sum_{j=k+1}^m a_{ij} x_i x_j##
If so, then since ##Q_m > 0##, how do we know this second term is not ##=> Q_m##?

You are having trouble here because either you only read the (last) half of what I wrote, or else have forgotten the first half when you read the second half. That is absolutely my last word on this topic.
 
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  • #27
Ray Vickson said:
You are having trouble here because either you only read the (last) half of what I wrote, or else have forgotten the first half when you read the second half. That is absolutely my last word on this topic.
Ah I get it. I was misreading the first part of that post. I had thought you were also adding in zeros into the last columns and rows of the A matrix for the ##Q_m## computation.
 
  • #28
pyroknife said:
Yes, I was saying it's equal for ##x_k## such that ##x_k## is the first ##k## elements of an arbitrary ##x##. For some reason I thought this was true?
Yes if you define x_k that way then it is true.
pyroknife said:
Because if so, then we have already proved ##\Delta_k ## is positive definite
No, that only proves the first part of post #20, you still need to prove that ##\Delta_k^{mxm}## is positive definite, right?
The proof to this latter part is what I've been trying to hint. It goes like this:

##(\vec y)^TA(\vec y) >0 \implies (J_k\vec x)^TA(J_k\vec x) = \vec x^T(J_k^TAJ_k)\vec x > 0##

If you can find a matrix Jk such that ##J_k^TAJ_k = \Delta_k^{mxm}## then that would prove the positive definiteness of ##\Delta_k^{mxm}## which would complete the proof. This matrix Jk is what I've been waiting for you to realize (it's the one I've been talking about which is similar to the identity).
 
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  • #29
Nathanael said:
Yes if you define x_k that way then it is true.

But that only proves the first part of post #20, you still need to prove that ##\Delta_k^{mxm}## is positive definite, right?
The proof to this latter part is what I've been trying to hint. It goes like this:

##(\vec y)^TA(\vec y) >0 \implies (J_k\vec x)^TA(J_k\vec x) = \vec x^T(J_k^TAJ_k)\vec x > 0##

If you can find a matrix Jk such that ##J_k^TAJ_k = \Delta_k^{mxm}## then that would prove the positive definiteness of ##\Delta_k^{mxm}## which would complete the proof. This matrix Jk is what I've been waiting for you to realize (it's the one I've been talking about which is similar to the identity).
OHHH. So earlier when you said a matrix that multiplies the matrix A to yield ##\Delta_k^{mxm}##, I thought you meant something like this: ##B*A = \Delta_k^{mxm}##. But now I get it; I hadn't realized that we needed to multiply to both the left and right size of A. Actually, this is basically just what Ray Vickson had said, but in matrix notation.

But I get it now, thanks everyone for all the help.
 
  • #30
pyroknife said:
OHHH. So earlier when you said a matrix that multiplies the matrix A to yield ##\Delta_k^{mxm}##, I thought you meant something like this: ##B*A = \Delta_k^{mxm}##.
Well I kind of did mean that, because as it turns out ##J_kA = J_k^TAJ_k= \Delta_k^{mxm}##

pyroknife said:
But I get it now, thanks everyone for all the help.
But wait! There's one last step in my proof, which is to find a Jk such that ##J_k^TAJ_k= \Delta_k^{mxm}##

I'll just say it; Jk is the mxm matrix with the kxk identity matrix in the top left and zeros everywhere else. The transpose of Jk is still Jk, and multiplying A by Jk on either the left or right (or both) gives ##\Delta_k^{mxm}##.
 
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  • #31
Nathanael said:
Well I kind of did mean that, because as it turns out ##J_kA = J_k^TAJ_k= \Delta_k^{mxm}##But wait! There's one last step in my proof, which is to find a Jk such that ##J_k^TAJ_k= \Delta_k^{mxm}##

I'll just say it; Jk is the mxm matrix with the kxk identity matrix in the top left and zeros everywhere else. The transpose of Jk is still Jk, and multiplying A by Jk on either the left or right (or both) gives ##\Delta_k^{mxm}##.
Hmmm, I said this earlier, but I tried it and it didn't work. Let me write it out.

Let's just assume ##A =
\begin{bmatrix}
1 & 1 & 1\\
1& 1 & 1\\
1& 1 & 1\\
\end{bmatrix}##

so ##\Delta_k^{mxm} =
\begin{bmatrix}
1 & 1 & 0\\
1& 1 & 0\\
0& 0 & 0\\
\end{bmatrix}##

But
##\begin{bmatrix}
1 & 0 & 0\\
0& 1 & 0\\
0& 0 & 0\\
\end{bmatrix}
\begin{bmatrix}
1 & 1 & 1\\
1& 1 & 1\\
1& 1 & 1\\
\end{bmatrix}
\neq \Delta_k^{mxm}##

But if we multiply this matrix on both sides, then we get ##\Delta_k^{mxm}##. So I don't think you can just multiply on one side?
So ##J_k A \neq J_k^T A J_k##
 
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  • #32
pyroknife said:
Hmmm, I said this earlier, but I tried it and it didn't work. Let me write it out.
...
But if we multiply this matrix on both sides, then we get ##\Delta_k^{mxm}##. So I don't think you can just multiply on one side?
So ##J_k A \neq J_k^T A J_k##
Oh darn! You're right! Multiplying on the left JkA says "get rid of the last m-k rows" and multiplying on the right AJk says "get rid of the last m-k columns." We need to do both in order to get ##\Delta_k^{mxm}##! Sorry for any confusion, I was looking at that wrongly.

o0) Luckily it doesn't change the proof.
 
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Related to Hermitian, positive definite matrices

1. What is a Hermitian matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. In other words, the elements of the matrix are symmetric about the main diagonal and the complex conjugate of each element is equal to the corresponding element.

2. What does it mean for a matrix to be positive definite?

A positive definite matrix is a Hermitian matrix where all of the eigenvalues are positive. This means that the matrix is always diagonalizable and all of its diagonal elements are positive.

3. How do you determine if a matrix is positive definite?

To determine if a matrix is positive definite, you can check if all of its eigenvalues are positive. Another way is to check if all of the principal minors (determinants of the upper-left submatrices) are positive.

4. What are some properties of Hermitian, positive definite matrices?

Some properties of Hermitian, positive definite matrices include:

  • They are always invertible.
  • They have positive eigenvalues.
  • They have a unique Cholesky decomposition.
  • They are always diagonalizable.

5. How are Hermitian, positive definite matrices used in mathematics and science?

Hermitian, positive definite matrices have many applications in mathematics and science, including:

  • They are used in optimization problems, such as in the method of conjugate gradients.
  • They are used in statistics, particularly in multivariate analysis and the calculation of confidence intervals.
  • They are used in physics, particularly in quantum mechanics and the study of energy levels.
  • They are used in machine learning algorithms, such as in the calculation of covariance matrices.

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