Notation in linear algebra and rule for square of matrix norm

In summary, the conversation discusses the definition of norm and the notation for it, specifically in the context of a matrix \Sigma and its inverse \Sigma^{-1}. The summary also includes a clarification on how the first equation changes to the second equation.
  • #1
lishrimp
2
0
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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  • #2
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.
 
  • #3
Thank you very much, micromass! :D
 

1. What is notation in linear algebra?

Notation in linear algebra refers to the symbols and mathematical expressions used to represent mathematical concepts and operations in linear algebra. These notations help to simplify and generalize complex mathematical ideas and make them easier to understand and manipulate.

2. What is a matrix norm?

A matrix norm is a measure of the size or magnitude of a matrix. It is analogous to the concept of absolute value in regular algebra. The norm of a matrix is a single number that represents how much the matrix "stretches" or "distorts" space when it is multiplied with another matrix or vector.

3. How is the norm of a matrix calculated?

The norm of a matrix is calculated by taking the square root of the sum of the squares of all the elements in the matrix. This is also known as the Frobenius norm or the Euclidean norm. Other types of matrix norms include the maximum norm, the induced norm, and the spectral norm. The specific formula for each type of norm may vary, but they all represent a way to measure the size of a matrix.

4. What is the rule for finding the square of a matrix norm?

The rule for finding the square of a matrix norm is to simply multiply the matrix by its transpose. This is known as the "norm squared" or the "squared norm" of the matrix. In mathematical notation, it can be written as ||A||2 = ATA. This rule is often used in linear algebra to simplify calculations and proofs.

5. How is matrix notation used in real-world applications?

Matrix notation is used in a variety of real-world applications, including engineering, physics, economics, and computer science. It provides a powerful and versatile way to represent and manipulate complex data and systems. Some common applications of matrix notation include solving systems of linear equations, data analysis and modeling, image and signal processing, and machine learning.

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