How can I prove that $\theta_p$ is a p-Norm?

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In summary, the conversation is about proving that the function $\theta_p:\mathbb{Q}\rightarrow \mathbb{R}$ defined as $x=p^{w(x)}u \mapsto c^{w(x)}$ is a p-Norm. The definition of a p-Norm is given as $x \neq 0, x=p^{w_p(x)}u \mapsto p^{-w_p(x)}$, and for $x=0 \Leftrightarrow w_p(x)=\infty$, $p^{-\infty}:=0$. The conversation delves into discussing the identities that must be satisfied by a p-Norm and how $\theta_p$ satisfies the first three identities. The question of
  • #1
evinda
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Hello! (Wave)

Let $c \in \mathbb{R}, 0<c<1$ and $p \in \mathbb{P}$. We consider the function $\theta_p:\mathbb{Q}\rightarrow \mathbb{R}$
$x=p^{w(x)}u \mapsto c^{w(x)}$.
Show that $\theta_p$ is a p-Norm.

How could I show this? (Thinking) :confused:
 
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  • #2
evinda said:
Hello! (Wave)

Let $c \in \mathbb{R}, 0<c<1$ and $p \in \mathbb{P}$. We consider the function $\theta_p:\mathbb{Q}\rightarrow \mathbb{R}$
$x=p^{w(x)}u \mapsto c^{w(x)}$.
Show that $\theta_p$ is a p-Norm.

How could I show this? (Thinking) :confused:

Hi evinda,

What do you mean by a $p$-Norm? Are you claiming that $\theta_p$ satisfies the strong triangle inequality $\theta_p(x + y) \le \max\{\theta_p(x), \theta_p(y)\}$?
 
  • #3
Euge said:
Hi evinda,

What do you mean by a $p$-Norm? Are you claiming that $\theta_p$ satisfies the strong triangle inequality $\theta_p(x + y) \le \max\{\theta_p(x), \theta_p(y)\}$?

According to my notes:

A $p-$ norm of $\mathbb{Q}_p$ is a function $||_p: \mathbb{Q}_p \to \mathbb{R}$

$$x \neq 0, x=p^{w_p(x)}u \mapsto p^{-w_p(x)}$$

$$\text{ For } x=0 \Leftrightarrow w_p(x)=\infty \\ p^{-\infty}:=0$$

Identities:

  1. $$|x|_p \geq 0 \text{ and } |x|_p=0 \Leftrightarrow x=0$$
  2. $$|xy|_p=|x|_p |y|_p$$
  3. $$|x+y|_p \leq \max \{ |x|_p, |y|_p\} \\ \leq |x|_p+|y|_p$$
    If $|x|_p \neq |y|_p \Rightarrow |x+y|_p=\max \{ |x|_p, |y|_p\}$
  4. $$\mathbb{Z}_p=\{ x \in \mathbb{Q}_p | |x|_p \leq 1 \}$$
  5. $$\mathbb{Z}_{p}^*=\{ x \in \mathbb{Z}_p | |x|_p=1 \}$$
 
  • #4
I don't even understand the question. If you are defining $p$-norm by the usual $p$-adic norm, how does it make sense to say that some other function $x \mapsto c^{\nu_p(x)}$ is a $p$-norm?

In any case, it is quite straightforward that $\theta_p : x \mapsto c^{\nu_p(x)}$ satisfies the strong triangle ineq : we know that $\nu_p(x + y) \geq \text{min}(\nu_p(x), \nu_p(y))$. Thus $\theta_p(x + y) = c^{\nu_p(x + y)} \leq c^{\min(\nu_p(x), \nu_p(y))} = \text{min}(c^{\nu_p(x)}, c^{\nu_p(y)}) = \text{min}(\theta_p(x), \theta_p(y))$ $\blacksquare$
 
  • #5
mathbalarka said:
I don't even understand the question. If you are defining $p$-norm by the usual $p$-adic norm, how does it make sense to say that some other function $x \mapsto c^{\nu_p(x)}$ is a $p$-norm?

In any case, it is quite straightforward that $\theta_p : x \mapsto c^{\nu_p(x)}$ satisfies the strong triangle ineq : we know that $\nu_p(x + y) \geq \text{min}(\nu_p(x), \nu_p(y))$. Thus $\theta_p(x + y) = c^{\nu_p(x + y)} \leq c^{\min(\nu_p(x), \nu_p(y))} = \text{min}(c^{\nu_p(x)}, c^{\nu_p(y)}) = \text{min}(\theta_p(x), \theta_p(y))$ $\blacksquare$

So, do I not have to show that $\theta_p$ satisfies all of the above $5$ identities? (Thinking)
 
  • #6
evinda said:
So, do I not have to show that $\theta_p$ satisfies all of the above $5$ identities? (Thinking)

I don't know. As I said, I don't understand your question. $\theta_p$ is certainly not a $p$-adic norm. So clarify what a $p$-norm is first.
 
  • #7
mathbalarka said:
I don't know. As I said, I don't understand your question. $\theta_p$ is certainly not a $p$-adic norm. So clarify what a $p$-norm is first.

The exercise asks to show that $\theta_p$ is a $p-$ norm.

The definition of $p-$norm, that is given in my notes, is the one I wrote in post #3.

That's why I thought, that I should show that $\theta_p$ satisfies the $5$ identities. :confused:

Do, $\theta_p$ satisfy the identities, indeed? (Thinking)
 
  • #8
That's what I have tried to prove that $\theta_p$ satisfies the first three identities:


  • For [tex]x \neq 0, x=p^{w(x)}u \mapsto c^{w(x)}>0[/tex], since [tex]c>0[/tex].
    What can I say for [tex]x=0[/tex], in order to show that [tex]|x|_{\theta_p}=0[/tex]? :confused:
  • [tex]x=p^{w(x)}u_1, y=p^{w(y)}u_2, u_1, u_2 \in \mathbb{Z}_p^*[/tex]

    [tex]|x|_{\theta_p}=c^{w(x)}[/tex]

    [tex]|y|_{\theta_p}=c^{w(y)}[/tex]

    [tex]|xy|_{\theta_p}=|p^{w(x)+w(y)} u_1u_2|_{\theta_p}=c^{w(x)+w(y)}=c^{w(x)} \cdot c^{w(y)}=|x|_{\theta_p} \cdot |y|_{\theta_p}[/tex]
  • [tex]x+y=p^{w(x)}u_1+p^{w(y)}u_2[/tex]

    Let [tex]w(x)>w(y) \Rightarrow |x|_{\theta_p} \neq |y|_{\theta_p}[/tex]

    Then, [tex]x+y=p^{w(x)}[p^{w(x)-w(y)}u_1+u_2][/tex]

    Therefore, [tex]|x+y|_{\theta_p}=c^{w(y)}=|y|_p=\max \{ |x|_p, |y|_p\}[/tex]

    If [tex]w(x)=w(y) \Rightarrow |x|_{\theta_p}=|y|_{\theta_p}[/tex].

    Then,

    [tex]x+y=p^{w(y)}[u_1+u_2][/tex]

    [tex]|x+y|_{\theta_p}=|p^{w(y)}(u_1+u_2)|_{\theta_p} \leq c^{w(y)}=\max \{ |x|_{\theta_p}, |y|_{\theta} \}[/tex]

What can I do, to show that [tex]|x|_{\theta_p}=0[/tex], when [tex]x=0[/tex]?
Also, is that what I have tried right or have I done something wrong? :confused:
 

FAQ: How can I prove that $\theta_p$ is a p-Norm?

What is a p-Norm?

A p-Norm is a mathematical concept used to measure the size or magnitude of a vector or a function. It is denoted by ||x||p and is defined as the p-th root of the sum of the p-th powers of the components of the vector or function.

How is a p-Norm calculated?

The p-Norm calculation involves taking the p-th root of the sum of the p-th powers of the components of the vector or function. For example, the p-Norm of a vector (x1, x2, ..., xn) is calculated as ||x||p = (|x1|p + |x2|p + ... + |xn|p)1/p.

What is the significance of the p value in a p-Norm?

The p value in a p-Norm determines the type of norm being calculated. For example, when p=1, it is called the Manhattan norm or the taxicab norm, when p=2, it is the Euclidean norm, and when p=∞, it is the maximum norm. Different p values yield different results and are used in different applications.

How does a p-Norm differ from other types of norms?

A p-Norm differs from other types of norms in terms of the way it is calculated and the results it provides. For example, the L1 norm or the Manhattan norm only takes into account the absolute values of the components, while the L2 norm or the Euclidean norm takes into account the squares of the components. In general, a p-Norm is a more generalized way of measuring size or magnitude compared to other norms.

What are some real-world applications of p-Norm?

p-Norms are commonly used in various fields such as physics, engineering, and data analysis. They are used to measure distances, calculate averages, and determine the convergence of a sequence. In engineering, p-Norms are used to measure the error in a numerical approximation. In data analysis, p-Norms are used to measure the similarity between two data points or to identify outliers.

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