Notation in linear algebra and rule for square of matrix norm

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The discussion addresses the notation related to matrix norms, specifically focusing on the interpretation of a norm defined by a matrix Σ. It clarifies that the norm can be expressed as the square root of a quadratic form involving Σ, leading to the conclusion that the squared norm equals the quadratic form itself. Additionally, participants seek clarification on the rules for expanding the square of a matrix norm, particularly for the expression ||A*B*C||^2. The explanation provided emphasizes that understanding these definitions is crucial for manipulating matrix norms correctly. Overall, the conversation enhances comprehension of matrix norms and their properties in linear algebra.
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 \Sigma is a matrix, then we can define a "norm" \|~\|_\Sigma by setting

\|\mathbf{x}\|_\Sigma=\sqrt{\mathbf{x}^T\Sigma \mathbf{x}}

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

\|\mathbf{x}\|_{\Sigma^{-1}}=\sqrt{\mathbf{x}^T\Sigma^{-1} \mathbf{x}}

So

\|\mathbf{x}\|_{\Sigma^{-1}}^2=\mathbf{x}^T\Sigma^{-1} \mathbf{x}

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