Prove that the dual norm is in fact a norm

In summary, the conversation discusses the definition of dual norm and the proof that it is a norm. The Hölder inequality is used to show the triangle inequality, and it is finally proven that the supremum-norm is indeed a norm.
  • #1
Dafe
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Homework Statement


Let [tex]||\cdot |[/tex]| denote any norm on [tex]\mathbb{C}^m[/tex]. The corresponding dual norm [tex]||\cdot ||'[/tex] is defined by the formula [tex]||x||^=sup_{||y||=1}|y^*x|[/tex].
Prove that [tex]||\cdot ||'[/tex] is a norm.

Homework Equations


I think the Hölder inequality is relevant: [tex]|x^*y|\leq ||x||_p ||y||_q, 1/p+1/q=1[/tex] with [tex]1\leq p, q\leq\infty[/tex]

The Attempt at a Solution


Since a norm is a function satisfying three properties, I need to show that they hold.

(1) [tex]||x||'=0[/tex] if and only if [tex]x=0[/tex].
(2) [tex]||\alpha x||'=|\alpha| ||x||^[/tex].
(3) [tex]||x+z||'\leq ||x||^+||z||^[/tex].

I manage to do (1) and (2) just fine, but the triangle inequality (3) is giving me problems.

I use the Hölder inequality to get the following:

[tex]||x+z||'=sup_{||y||=1}|y^*(x+z)|\leq ||y|| ||x+z||=||x+z||[/tex]

[tex]||x||'=sup_{||y||=1}|y^*x|\leq ||y|| ||x|| =||x||[/tex]

[tex]||z||'=sup_{||y||=1}|y^*z|\leq ||y|| ||z|| =||z||[/tex]

(1) [tex]||x||'\leq ||x||[/tex]
(2) [tex]||z||'\leq ||z||[/tex]
(3) [tex]||x||'+||z||' \leq ||x||+||z||[/tex]

I also know that
(4) [tex]||x+z|| \leq ||x||+||z||[/tex]

I am unable to show that [tex]||x+z||\leq ||x||'+||z||'[/tex] which I think I must if I am to prove the triangle inequality.

Any help is appreciated.
 
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  • #2
[tex]||x+z||'=sup_{||y||=1} |y^*(x+z)| = sup_{||y||=1} | y^*x + y^*z | \leq sup_{||y||=1} | y^*x| + sup_{||y||=1} |y^*z | = ||x||' + ||z||' [/tex]

The supremum-norm is a norm.
 
Last edited:
  • #3
Ah, didn't think of it that way. Thank you very much.
 

1. What is the definition of a dual norm?

The dual norm of a normed vector space is a function that measures the size or magnitude of a vector in the dual space, which is the set of all linear functionals on the vector space.

2. How is the dual norm related to the original norm?

The dual norm is defined as the supremum of the inner product of a vector with all elements of the dual space, divided by the norm of the vector in the original space. This means that the dual norm is a measure of the largest possible value that a linear functional can take on a given vector, relative to the norm of that vector.

3. Why is the dual norm considered a norm?

The dual norm satisfies all three properties of a norm: positivity, homogeneity, and the triangle inequality. This can be proven using the definition of the dual norm and the properties of the original norm.

4. Can the dual norm be different from the original norm?

Yes, in general, the dual norm will be different from the original norm. For example, the dual norm of the L1-norm (which measures the absolute value of the elements of a vector) is the L∞-norm (which measures the maximum absolute value of the elements of a vector).

5. What are the applications of the dual norm?

The dual norm is used in convex analysis and optimization, as well as in the study of duality in functional analysis. It also has applications in areas such as signal processing, machine learning, and control theory.

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