Are All Eigenvalues of a Matrix Between Defined Limits?

In summary, if A is symmetric, then A-λI is positive definite if and only if all eigenvalues of A are >λ. If A-λI is negative definite, then all the eigenvalues of A are between zero and eight.
  • #1
angelz429
24
0
[SOLVED] Positive Definite Matrices

Homework Statement



a) If A is Symmetric show that A-λI is positive definite if and only if all eigenvalues of A are >λ, and A-λI is negative definite if and only if all eigenvalues of A are <λ.

b) Use this result to show that all the eigenvalues of
[ 5 2 -1 0]
[ 2 5 0 1]
[-1 0 5 -2]
[ 0 1 -2 5]
are between zero and eight.



Homework Equations



If all eigenvalues of A>0, A is positive definite.


The Attempt at a Solution



i'm not sure where to start
 
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  • #2
here's a hint: since A is symmetric, you can get a set of eigenvectors of A which are an orthonormal basis for the vector space.
 
  • #3
hmm... i understand what you are saying, but I'm not sure how to apply it. My main problem is with proving part a.
 
  • #4
to show that a matrix M is positive definite, it is sufficient to show that v^T M v > 0 for any vector v in an orthonormal basis. since you can get such a basis with eigenvectors of A, you just need to show that for any eigenvector v of A, v^T (A-λI) v > 0. remember that you know how A acts on eigenvectors.
 
  • #5
angelz429 said:
hmm... i understand what you are saying, but I'm not sure how to apply it. My main problem is with proving part a.

How would you find the eigenvalues of A-lambda*I? How would you find the eigenvalues of A for that matter?
 
  • #6
Dick said:
How would you find the eigenvalues of A-lambda*I? How would you find the eigenvalues of A for that matter?

like any other eigenvalue i suppose do det((A-lambda*I)-alpha*I)=0 and solve for alpha.
 
  • #7
Right. So when you do that alpha is an eigenvalue of A-lambda*I. Do you see that makes (lambda+alpha) an eigenvalue of A? So what's the relation between the eigenvalues of A and those of A-lambda*I?
 
  • #8
ok, so det(A-lambda*I-alpha*I) implies det (A-alpha*lamba*I) therefore alpha is an eigenvlaue od A-lambda*I and alpha*lambda is an eigenvalue of A.

But what does this say about the relationship between alpha and lambda? I'm supposed to see that if alpha > lambda A is positive definite & is alpha < lambda A is negative definite.

If alpha > lambda, alpha*lambda > lambda
If alpha < lambda, alpha*lambda can be < lambda

That's all I can see. How does this show that all alpha*lambda> 0 or alpha*lambda<0?
 
  • #9
det(A-lambda*I-alpha*I)=det(A-(lambda+alpha)*I). '+', not '*'! If alpha+lambda is an eigenvalue of A when alpha is an eigenvalue A-lambda*I, I would say that the eigenvalues of A-lambda*I are obtained by subtracting lambda from the eigenvalues of A. Wouldn't you agree??
 
  • #10
oh riiiight! Thanks!... but how can we guarantee that alpha + lambda is > 0?
 
  • #11
errr i mean that alpha - lambda is > 0
 
  • #12
angelz429 said:
errr i mean that alpha - lambda is > 0

You'd better put some conditions on the eigenvalues of A. Look at what you are trying to prove.
 
  • #13
Alright, so I understand the first part... now how do I use this to show that all the eigenvalues of B are between zero & eight? I just checked that B is positive definite...
 
  • #14
ahhh nevermind... i got it!
 
  • #15
Thanks!
 

Related to Are All Eigenvalues of a Matrix Between Defined Limits?

What is a positive definite matrix?

A positive definite matrix is a square matrix where all its eigenvalues are positive. This means that for any non-zero vector, the dot product of the vector with the matrix results in a positive number.

What are the properties of a positive definite matrix?

Some of the properties of a positive definite matrix include: all its eigenvalues are real and positive, it is symmetric, and all its principal minors are positive.

How is a positive definite matrix used in mathematics?

A positive definite matrix is used in many areas of mathematics, such as optimization, statistics, and differential equations. It is also a key concept in linear algebra and has applications in computer science and physics.

How do you determine if a matrix is positive definite?

To determine if a matrix is positive definite, you can check if all its eigenvalues are positive, or if all its principal minors are positive. Alternatively, you can use the Sylvester's criterion, which states that a matrix is positive definite if and only if all its leading principal minors are positive.

Can a square matrix be both positive definite and positive semidefinite?

Yes, a square matrix can be both positive definite and positive semidefinite. A positive semidefinite matrix is a square matrix where all its eigenvalues are non-negative. Therefore, a positive definite matrix, where all its eigenvalues are positive, is a subset of positive semidefinite matrices.

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