Notation in linear algebra and rule for square of matrix norm

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SUMMARY

The discussion focuses on the notation used in linear algebra, specifically regarding the norm of a matrix and its expansion. The norm defined by a matrix \(\Sigma\) is expressed as \(\|\mathbf{x}\|_\Sigma=\sqrt{\mathbf{x}^T\Sigma \mathbf{x}}\). For the inverse matrix \(\Sigma^{-1}\), the norm is given by \(\|\mathbf{x}\|_{\Sigma^{-1}}=\sqrt{\mathbf{x}^T\Sigma^{-1} \mathbf{x}}\), leading to the conclusion that \(\|\mathbf{x}\|_{\Sigma^{-1}}^2=\mathbf{x}^T\Sigma^{-1} \mathbf{x}\) is valid by definition.

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lishrimp
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Hi.

I have a few simple questions.

eq1.jpg
(<- sorry, please click this image.)

1. What does the notation in the red circle mean?

2. Is there a rule for expanding square of norm? (e.g. || A*B*C ||^2)
I don't really understand how the first eq. changes to the second eq.

Thanks. :)
 
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Hi lishrimp! :smile:

lishrimp said:
Hi.

I have a few simple questions.

View attachment 37815 (<- sorry, please click this image.)

1. What does the notation in the red circle mean?

2. Is there a rule for expanding square of norm? (e.g. || A*B*C ||^2)
I don't really understand how the first eq. changes to the second eq.

Thanks. :)

My guess:

If [itex]\Sigma[/itex] is a matrix, then we can define a "norm" [itex]\|~\|_\Sigma[/itex] by setting

[tex]\|\mathbf{x}\|_\Sigma=\sqrt{\mathbf{x}^T\Sigma \mathbf{x}}[/tex]

In your case, the matrix is [itex]\Sigma^{-1}[/itex], so the norm is

[tex]\|\mathbf{x}\|_{\Sigma^{-1}}=\sqrt{\mathbf{x}^T\Sigma^{-1} \mathbf{x}}[/tex]

So

[tex]\|\mathbf{x}\|_{\Sigma^{-1}}^2=\mathbf{x}^T\Sigma^{-1} \mathbf{x}[/tex]

So that equality is true by definition.
 
Thank you very much, micromass! :D
 

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