Proof of a normed space

In summary: V_{\infty}(F). This can be proven using the Minkowski inequality, which states that for any real numbers a and b, we have (a+b)^{p}\leq a^{p}+b^{p}. Applying this to our norm, we have:\left\|x+y\right\|_{p}=\left[\sum^{\infty}_{k=1}\left|\alpha^{i}+\beta^{i}\right|^{p}\right]^{1/p}\leq\left[\sum^{\infty}_{k=1}\
  • #1
cabin5
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Homework Statement


The norm is defined by [tex]\left\|x\right\|_{p}=\left[\sum^{\infty}_{k=1}\left|\alpha^{i}\right|^{p}\right]^{1/p}[/tex]
where [tex]x=(\alpha^{1},\alpha^{2},...,\alpha^{n})[/tex]

Prove that this is a norm on [tex]V_{\infty}(F)[/tex]

Homework Equations


All conditions satisfied for a normed space.


The Attempt at a Solution


Well, I proved the first condition which is

i)
[tex]\left\|x\right\|_{p}=\left\{\left|\alpha^{1}\right|^{p}+\left|\alpha^{2}\right|^{p}+...+\left|\alpha^{n}\right|^{p}+...\right\}^{1/p}[/tex]

This must be positive definitive, therefore [tex]\left\|x\right\|_{p}>0[/tex]

On the second condition I don't know whether taking the product of this norm with a [tex]\beta\in F[/tex] since the sum is infinite. I got stuck at this point.

And also I presume, for the third condition, Minkowski inequality cannot be used anymore to prove it.
 
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  • #2


Any help would be greatly appreciated!

it is important to understand the fundamental properties and definitions of mathematical concepts in order to apply them accurately and effectively in research. In this case, we are looking at the norm defined by \left\|x\right\|_{p}=\left[\sum^{\infty}_{k=1}\left|\alpha^{i}\right|^{p}\right]^{1/p}.

To prove that this is a norm on V_{\infty}(F), we need to show that it satisfies the three conditions for a normed space: positivity, absolute homogeneity, and the triangle inequality.

For the first condition, we can see that \left\|x\right\|_{p} is always positive since it is a sum of non-negative terms raised to the power of p and then taking the pth root. This means that \left\|x\right\|_{p}>0 for all x\in V_{\infty}(F).

Next, for the second condition of absolute homogeneity, we need to show that \left\|\beta x\right\|_{p}=|\beta|\left\|x\right\|_{p} for all x\in V_{\infty}(F) and \beta\in F. Since the sum is infinite, we cannot simply take the product of the norm with \beta. Instead, we can use the fact that raising a number to the power of p and then taking the pth root is the same as taking the absolute value. Therefore, we have:

\left\|\beta x\right\|_{p}=\left[\sum^{\infty}_{k=1}\left|\beta\alpha^{i}\right|^{p}\right]^{1/p}=\left[\sum^{\infty}_{k=1}|\beta|^{p}\left|\alpha^{i}\right|^{p}\right]^{1/p}=|\beta|\left[\sum^{\infty}_{k=1}\left|\alpha^{i}\right|^{p}\right]^{1/p}=|\beta|\left\|x\right\|_{p}

This shows that the norm is absolutely homogeneous.

Finally, for the third condition of the triangle inequality, we need to show that \left\|x+y\right\|_{p}\leq\left
 

1. What is a normed space?

A normed space is a mathematical concept used in the field of functional analysis. It is a vector space equipped with a mathematical structure called a norm, which is a function that assigns a non-negative length or size to each vector in the space.

2. What is the purpose of proving a normed space?

The purpose of proving a normed space is to provide a rigorous mathematical framework for studying and analyzing vector spaces. It allows for the definition of important concepts such as convergence, completeness, and continuity, which are crucial in many areas of mathematics and its applications.

3. How is a normed space different from a metric space?

A metric space is a mathematical concept where distance between points is defined, while a normed space is a vector space with an additional structure of a norm. The norm in a normed space is a special type of metric, but it also allows for the definition of vector operations such as addition and scalar multiplication.

4. Are all vector spaces normed spaces?

No, not all vector spaces are normed spaces. A normed space has additional structure in the form of a norm, which is not necessarily present in all vector spaces. In fact, not all vector spaces are metric spaces either.

5. What are some examples of normed spaces?

Some examples of normed spaces include Euclidean spaces, function spaces, and sequence spaces. In particular, the space of real or complex-valued continuous functions on a closed and bounded interval is a normed space with the supremum norm. The space of square-integrable functions is also a normed space with the L2 norm.

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