How can I show that K is positive-definite?

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  • Thread starter evinda
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In summary, the conversation is about a question regarding the proof of the Cholesky decomposition. The speaker asks how to prove that a matrix, K, is positive definite. They mention that they have to use the condition x^TAx > 0, and suggest using the matrix P to show that K is positive definite.
  • #1
evinda
Gold Member
MHB
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Hi! :)

I have also an other question about the proof of the Cholesky decomposition.
We write A like that:
$A=\begin{bmatrix}
d & u^{T}\\
u & H
\end{bmatrix}=\begin{bmatrix}
\sqrt d & 0\\
\frac{u}{\sqrt d} & I_{n-1}
\end{bmatrix}\begin{bmatrix}
1 & 0\\
0 & K
\end{bmatrix}\begin{bmatrix}
\sqrt d & \frac{u^{T}}{\sqrt{d}}\\
0 & I_{n-1}
\end{bmatrix}$

where $K=H-\frac{1}{d}uu^{T}$

and then we suppose that $K$ is symmetric and positive-definite,to use the assumption step(that is valid for $(n-1)x(n-1)$ symmetric and positive-definite matrices.
But...then I have to prove that $K$ is positive-definite.How can I do this?
 
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  • #2
evinda said:
Hi! :)

I have also an other question about the proof of the Cholesky decomposition.
We write A like that:
$A=\begin{bmatrix}
d & u^{T}\\
u & H
\end{bmatrix}=\begin{bmatrix}
\sqrt d & 0\\
\frac{u}{\sqrt d} & I_{n-1}
\end{bmatrix}\begin{bmatrix}
1 & 0\\
0 & K
\end{bmatrix}\begin{bmatrix}
\sqrt d & \frac{u^{T}}{\sqrt{d}}\\
0 & I_{n-1}
\end{bmatrix}$

where $K=H-\frac{1}{d}uu^{T}$

and then we suppose that $K$ is symmetric and positive-definite,to use the assumption step(that is valid for $(n-1)x(n-1)$ symmetric and positive-definite matrices.
But...then I have to prove that $K$ is positive-definite.How can I do this?

Do I have to use the condition $x^{T}Ax>0$ ?
 
  • #3
evinda said:
Hi! :)

I have also an other question about the proof of the Cholesky decomposition.
We write A like that:
$A=\begin{bmatrix}
d & u^{T}\\
u & H
\end{bmatrix}=\begin{bmatrix}
\sqrt d & 0\\
\frac{u}{\sqrt d} & I_{n-1}
\end{bmatrix}\begin{bmatrix}
1 & 0\\
0 & K
\end{bmatrix}\begin{bmatrix}
\sqrt d & \frac{u^{T}}{\sqrt{d}}\\
0 & I_{n-1}
\end{bmatrix}$

where $K=H-\frac{1}{d}uu^{T}$

and then we suppose that $K$ is symmetric and positive-definite,to use the assumption step(that is valid for $(n-1)x(n-1)$ symmetric and positive-definite matrices.
But...then I have to prove that $K$ is positive-definite.How can I do this?
The matrix $P = \begin{bmatrix} \sqrt d & 0\\ \frac1{\sqrt d}u & I_{n-1} \end{bmatrix}$ is invertible, because its determinant is the product of its diagonal entries, namely $\sqrt d.$ But $A = P \begin{bmatrix} 1 & 0\\ 0 & K \end{bmatrix}P^*,$ and $A$ is positive definite. Therefore $\begin{bmatrix} 1 & 0\\ 0 & K \end{bmatrix} = P^{-1}A(P^{-1})^*$ is positive definite. Hence $K$, which is a corner of that matrix, is also positive definite.
 

Related to How can I show that K is positive-definite?

1. How do I determine if a matrix K is positive-definite?

To show that a matrix K is positive-definite, we need to check two conditions: 1) all eigenvalues of K are positive, and 2) K is symmetric. If both conditions are satisfied, then K is positive-definite.

2. Can a matrix K be both positive-definite and positive-semidefinite?

No, a matrix cannot be both positive-definite and positive-semidefinite. A matrix is positive-definite if all of its eigenvalues are positive, while a matrix is positive-semidefinite if all of its eigenvalues are non-negative. Therefore, a positive-definite matrix cannot have any zero eigenvalues, which is a requirement for a positive-semidefinite matrix.

3. What is the importance of a matrix being positive-definite?

A positive-definite matrix is important because it has many useful properties in linear algebra and optimization. For example, it guarantees that a quadratic function will have a unique minimum value, and it is a necessary condition for a matrix to be invertible.

4. How do I prove that a matrix K is positive-definite?

To prove that a matrix K is positive-definite, you can use several methods such as the Cholesky decomposition, the Sylvester's criterion, or the eigenvalue test. Each method involves checking different conditions, so you can choose the one that is most suitable for your problem.

5. Can a non-square matrix be positive-definite?

No, a non-square matrix cannot be positive-definite. A matrix needs to be square in order to have eigenvalues, and the definition of a positive-definite matrix requires all eigenvalues to be positive. Therefore, a non-square matrix cannot fulfill the conditions to be considered positive-definite.

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