Proving the Normality of ||A||_1 for A in Mat_n

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In summary, the conversation discusses the definition of the norm ||A||_1 for matrices in the space Mat_n. It is defined as the supremum of the quotient of the norm of A applied to a vector x and the norm of x itself. The conversation then goes on to discuss the proof that this norm satisfies all the properties of a norm. It is also mentioned that the supremum always exists and that the norm would not satisfy the triangle inequality if it was defined for a fixed vector instead of all vectors of unit norm.
  • #1
Dragonfall
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"Let [tex]Mat_n[/tex] denote the space of [tex]n\times n[/tex] matrices. For [tex]A\in Mat_n[/tex], define the norms [tex]||A||_1[/tex] as follows:

[tex]||A||_1=\sup_{0\neq x\in\mathbb{R}^n}\frac{||Ax||}{||x||}[/tex],

where ||x|| is the usual Euclidean norm.

Prove that this norm is really a norm (triangle ineq, etc)"

I don't know how to even prove that the supremum exists.
 
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  • #2
Supremums always exist. That's why the extended real numbers are so nice! :smile:

I don't see why you would need to prove the supremum is finite, but you could always start by trying to solve simplified problems. (e.g. pick nice matrices, or restrict the supremum to a nice set of vectors) Or, you could try invoking interesting properties about the map x->Ax.
 
  • #3
Would it be a norm if instead of looking at the sup over all x, you looked at some fixed x?
 
  • #4
If v is any nonzero vector, then v' = v/|v| is a parallel vector to v of unit norm. Observe that |Av|/|v| = |A(|v|v')|/|v| = ||v|Av'|/|v| (by linearity of A) = |v|(|Av'|/|v|) = |Av'| = |Av'|/1 = |Av'|/|v'|. So the above definition for the norm of A is equivalent to the supremum of |Ax| over all x of unit norm. The set of x of unit norm is the unit (n-1)-sphere, which is compact in the usual topology of Rn, and you can prove that the function which maps x in the unit sphere to |Ax| is continuous by showing that it is the composition of two functions, the Euclidean norm function and the function A, and that each of these functions are continuous. Since this composition is a continuous function from a compact set to R, the extreme value theorem tells you that it obtains a maximum, which in turn tells you that the supremum exists.

Proving that this function |.|1 really does satisfy the norm axioms is easy, especially after realizing that:

[tex]||A||_1 = \sup _{x\in \mathbb{R}^n,\, ||x|| = 1}||Ax||[/tex]
 
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  • #5
No, it wouldn't be a norm if you looked at some fixed x. The map on the plane A(x,y) = x will map (0,1) to 0, so if we define the "norm" of A as |A(0,1)|, then we'd get a non-zero matrix with zero "norm", contradicting the norm axioms.

Since a norm, by definition, has the reals as its codomain (and not the extended reals), you do have to verify that the supremum always exists.
 
  • #6
Sorry, I meant would it satisfy the triangle inequality. That's the part I was trying to show how to prove.
 
  • #7
Ok, I got it. Thanks.
 

1. What does "proving the normality of ||A||_1 for A in Mat_n" mean?

Proving the normality of ||A||_1 for A in Mat_n refers to demonstrating that the 1-norm of a matrix A, denoted as ||A||_1, follows the properties of a normal distribution when A is an n x n matrix. This involves showing that the 1-norm is symmetric, obeys the triangle inequality, and satisfies the scalar multiplication property.

2. Why is it important to prove the normality of ||A||_1 for A in Mat_n?

Proving the normality of ||A||_1 for A in Mat_n is important because it allows us to use statistical methods and techniques that are based on the assumption of normality. Additionally, it provides a measure of the distribution of values in a matrix, which can be useful in various applications such as data analysis and machine learning.

3. What are the properties of a normal distribution?

The properties of a normal distribution include symmetry, a bell-shaped curve, and the majority of values falling within three standard deviations of the mean. It is also characterized by a mean and standard deviation, and its probability distribution function is given by the famous bell-shaped curve equation: f(x) = (1/(σ√2π))e^(-(x-µ)^2/(2σ^2)).

4. How is the normality of ||A||_1 for A in Mat_n proven?

The normality of ||A||_1 for A in Mat_n can be proven using mathematical techniques such as induction and proof by contradiction. It involves showing that the 1-norm satisfies the properties of a normal distribution, as well as using matrix operations and properties to demonstrate the equivalence between ||A||_1 and the standard normal distribution.

5. Are there any limitations to proving the normality of ||A||_1 for A in Mat_n?

Yes, there are limitations to proving the normality of ||A||_1 for A in Mat_n. One of the main limitations is that it only applies to matrices with real values. Additionally, the proof may become more complex as the size of the matrix increases. It is also important to note that even if a matrix satisfies the properties of a normal distribution, it may not necessarily be considered a normal matrix in the strict mathematical sense.

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