Symmetric positive definite matrix

In summary, a positive-definite matrix renders the quadratic form Q(x) = x^T A x = \sum_i a_{ii} x_i^2 + 2 \sum_{i < j} a_{ij} x_i x_j positive for all vectors ##x## that are not the zero vector. The largest element on the diagonal is |a_{12}|. If vectors of the form ##\tilde{x} = (x_1, x_2, 0, 0, \ldots,0)^T## are given, then Q(\tilde{x}) = a_{11} x_1^2 + 2
  • #1
rendinat
8
0

Homework Statement



In a symmetric positive definite matrix, why does max{|aij|}=max{|aii|}

Homework Equations



|aij|≤(aii+ajj)/2

The Attempt at a Solution



max{|aij|}≤max{(aii+ajj)/2

max{|aij|}≤max{aii/2}+max{ajj/2}

max{|aij|}≤[itex]\frac{1}{2}[/itex]max{aii}+[itex]\frac{1}{2}[/itex]max{ajj}

then I am stuck! :(
 
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  • #2
Try applying ##a_{ii} \leq |a_{ii}| \leq \max |a_{ii}|## to the original inequality.
 
  • #3
But how does that help with the equality I need?
 
  • #4
rendinat said:

Homework Statement



In a symmetric positive definite matrix, why does max{|aij|}=max{|aii|}

Homework Equations



|aij|≤(aii+ajj)/2

The Attempt at a Solution



max{|aij|}≤max{(aii+ajj)/2

max{|aij|}≤max{aii/2}+max{ajj/2}

max{|aij|}≤[itex]\frac{1}{2}[/itex]max{aii}+[itex]\frac{1}{2}[/itex]max{ajj}

then I am stuck! :(

Assume that the word "symmetric" is always present in the following. Remember that a positive-definite matrix is, by definition, one that renders the quadratic form
[tex] Q(x) = x^T A x = \sum_i a_{ii} x_i^2 + 2 \sum_{i < j} a_{ij} x_i x_j [/tex]
positive for all vectors ##x## that are not the zero vector. You are being asked to show that for a positive-definite matrix, the largest element occurs on the diagonal (in terms of magnitudes, that is). So, assume the contrary: that the largest element is not on the diagonal. By re-labelling if necessary we can assume that ##|a_{12}|## is the largest element. Now look at vectors of the form ##\tilde{x} = (x_1, x_2, 0, 0, \ldots,0)^T##, which give
[tex] Q(\tilde{x}) = a_{11} x_1^2 + 2 a_{12} x_1 x_2 + a_{22}x_2^2.[/tex] Now try to show that if ##|a_{12}| > a_{11}## and ##|a_{12}| > a_{22}## then we can find ##(x_1,x_2) \neq (0,0)## that makes ##Q(\tilde{x}) < 0##.
 
  • #5
By proving that it is not greater in that instance still doesn't prove they are equal, does it?
 
  • #6
rendinat said:
By proving that it is not greater in that instance still doesn't prove they are equal, does it?

I have no idea what you are talking about. I am just trying to derive a contradiction by assuming an off-diagonal element is maximal.
 
  • #7
I am trying to prove that max{|aij|} = max{|aii|}

Maybe I am going about it incorrectly and that is the problem. Where would you begin to try to prove these are equal?
 
  • #8
rendinat said:
But how does that help with the equality I need?
I can't do the problem for you. What do you get when you plug in the fact that ##a_{ii} \leq \max |a_{ii}|## into ##|a_{ij}| \leq (a_{ii} + a_{jj}) / 2##?
 
  • #9
|aij|≤(aii +ajj)/2≤(max{aii} +ajj)/2 Is this what you mean?

So I shouldn't do the steps I did to get to? max{|aij|}≤[itex]\frac{1}{2}[/itex]max{aii}+[itex]\frac{1}{2}[/itex]max{ajj}
 
  • #10
rendinat said:
So I shouldn't do the steps I did to get to? max{|aij|}≤[itex]\frac{1}{2}[/itex]max{aii}+[itex]\frac{1}{2}[/itex]max{ajj}
In fact, it should be ##\max |a_{ij}| \leq (\max |a_{ii}| + \max |a_{jj}|) / 2##.

What can you say about ##\max |a_{ii}|## and ##\max |a_{jj}|##?
 
  • #11
Aren't they equal and I get that max{|aij|}≤max{aii}
 
  • #12
rendinat said:
Aren't they equal and I get that max{|aij|}≤max{aii}
You dropped the absolute values on the right hand side. It should be ##\max |a_{ij}| \leq \max |a_{ii}|##. Can you see why this gives you the result you need?
 
  • #13
That has been my question, how can I show/prove that they are equal?
 
  • #14
If you can show the opposite inequality, that will suffice, right? In other words, is it true that
$$\max |a_{ii}| \leq \max |a_{ij}|$$
 
  • #15
jbunniii said:
You dropped the absolute values on the right hand side. It should be ##\max |a_{ij}| \leq \max |a_{ii}|##. Can you see why this gives you the result you need?

No need for absolute values on the right. If A is positive-definite we MUST have ##a_{ii} > 0## for all ##i##. If we weaken the assumption to positive-semidefinite, we still have ##a_{ii} \geq 0## for all ##i##.
 
  • #16
Ray Vickson said:
No need for absolute values on the right. If A is positive-definite we MUST have ##a_{ii} > 0## for all ##i##. If we weaken the assumption to positive-semidefinite, we still have ##a_{ii} \geq 0## for all ##i##.
True, good point. For this problem, it might be clearer to use the absolute values anyway, even if they are redundant for the diagonal elements.
 

FAQ: Symmetric positive definite matrix

1. What is a symmetric positive definite matrix?

A symmetric positive definite matrix is a square matrix where all the eigenvalues are positive and the matrix is equal to its transpose. This means that all the diagonal entries are positive and the off-diagonal entries are equal. It is a special type of matrix that has many important properties in linear algebra and is commonly used in various scientific fields such as statistics, physics, and engineering.

2. How is a symmetric positive definite matrix different from a regular matrix?

A symmetric positive definite matrix has specific properties that make it unique from a regular matrix. In addition to all the eigenvalues being positive, a symmetric positive definite matrix is also invertible and its inverse is also symmetric positive definite. This means that it has a unique solution for a system of linear equations and is often used in optimization problems.

3. Can a symmetric positive definite matrix have negative elements?

No, a symmetric positive definite matrix cannot have negative elements. As mentioned earlier, all the diagonal entries must be positive, and the off-diagonal entries must be equal. If any element in the matrix is negative, it would violate these conditions and the matrix would no longer be symmetric positive definite.

4. How is a symmetric positive definite matrix used in statistics?

In statistics, a symmetric positive definite matrix is commonly used in multivariate analysis and regression. It is used to represent the covariance matrix of a set of random variables and is essential in calculating the multivariate normal distribution. It is also used in various statistical models, such as the Mahalanobis distance, which measures the distance between two data points.

5. How can I determine if a matrix is symmetric positive definite?

One way to determine if a matrix is symmetric positive definite is to calculate its eigenvalues. If all the eigenvalues are positive, then the matrix is symmetric positive definite. Another way is to check if the matrix satisfies the conditions for symmetry and positivity mentioned earlier. Additionally, there are various numerical methods and algorithms that can be used to determine if a matrix is symmetric positive definite.

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