Proof that (x^t)Ax>0 if Eigenvalues of Matrix A > 0

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Discussion Overview

The discussion revolves around the proof of the statement that if a matrix A has all eigenvalues greater than zero, then the expression (x^t)Ax is greater than zero for any non-zero vector x. The scope includes theoretical exploration and mathematical reasoning.

Discussion Character

  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant proposes that if the eigenvalues of matrix A are greater than zero, then (x^t)Ax can be expressed as a product involving an eigenvalue and x, suggesting it should be positive for non-zero x.
  • Another participant questions the clarity of the initial proof, particularly regarding the reference to eigenvalues and points out that not all matrices are diagonalizable.
  • A different participant challenges the initial claim, providing a counterexample with a specific matrix A that has positive eigenvalues but yields a negative result for (x^t)Ax with a particular vector x.
  • This counterexample is reiterated, emphasizing that the relationship holds under specific conditions, such as when A is symmetric or when (A+A*)/2 is positive definite.

Areas of Agreement / Disagreement

Participants do not reach a consensus. There are competing views regarding the validity of the initial claim, with some asserting it is incorrect under certain conditions while others attempt to defend it.

Contextual Notes

The discussion highlights limitations related to the assumptions about matrix properties, such as diagonalizability and symmetry, which affect the validity of the claims made.

megaman
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I am looking for a rather simple proof that if the matrix A has eigenvalues>0, then (x^t)Ax>0 for any vector x not 0.

My first tought was if the eigenvalues are bigger than 0, then (x^t)Ax=(x^t)"eigenvaulue"x="eigenvalue"(x^t)x>0, if x is nonzero and eigenvalues is bigger than 0.

Is this proof good enough? I am a little unsure, because I think I have not proven it for all x, just the eigenvectors.
 
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I'm not sure what you mean by '(x^t)"eigenvaulue" x'. which eigenvalue?

It is true that if A is diagonalizable, then it is similar to a diagonal matrix having the eigenvalues on the diagonal.

For example, if A itself is
[tex]A= \begin{bmatrix}a_1 & 0 \\ 0 & a_2\end{bmatrix}[/tex]
where [itex]a_1[/itex] and [itex]a_2[/itex] are both positive, we have
[tex]x^*Ax= \begin{bmatrix}x_1 & x_2 \end{bmatrix}\begin{bmatrix}a_1 & 0 \\ 0 & a_2\end{bmatrix} \begin{bmatrix}x_1 \\ x_2\end{bmatrix}[/tex]
[tex]= a_1x_1^2+ a_2x_2^2[/itex] <br /> which is positive because it is the sum of two positive numbers (or one positive number and 0).<br /> <br /> But not all matrices are diagonalizable.[/tex]
 
You'll have a hard time proving this statement because it's not true.

Consider the following matrix:

[tex] A= \begin{bmatrix}9 & 5.5 \\ 1 & 1\end{bmatrix}[/tex]

The eigenvalues of A are both positive (verify).

Let x = (1,-3.1)T

Then
[tex]x^*Ax= \begin{bmatrix} 1 & -3.1 \end{bmatrix}\begin{bmatrix}9 & 5.5 \\ 1 & 1\end{bmatrix} \begin{bmatrix}1 \\ -3.1\end{bmatrix} = -1.54[/tex]

The relationship you describe is true a) if A is symmetric or b) if (A+A*)/2 is positive definite.
 
hgfalling said:
You'll have a hard time proving this statement because it's not true.

Consider the following matrix:

[tex] A= \begin{bmatrix}9 & 5.5 \\ 1 & 1\end{bmatrix}[/tex]

The eigenvalues of A are both positive (verify).

Let x = (1,-3.1)T

Then
[tex]x^*Ax= \begin{bmatrix} 1 & -3.1 \end{bmatrix}\begin{bmatrix}9 & 5.5 \\ 1 & 1\end{bmatrix} \begin{bmatrix}1 \\ -3.1\end{bmatrix} = -1.54[/tex]

The relationship you describe is true a) if A is symmetric or b) if (A+A*)/2 is positive definite.
Well done.
 

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