Show $\|.\|$ is an Norm: Prove Triangle Inequality

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In summary, the norm $\|.\|_2$ is equal to the sum of the norms $\|(x,y)\|$ and $\|(x^{\prime},y^{\prime})\|$
  • #1
Julio1
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If $\|(x,y)\|=\sqrt{x^2+4y^2}.$ Show that $\|.\|$ is an norm.

Hello MHB! :)

The cases $\|(x,y)\|\ge 0$ and $\|\lambda (x,y)\|=|\lambda|\|(x,y)\|$ are obvious, but the case $\|(x,y)+(x',y')\|\le \|(x,y)\|+\|(x',y')\|$ I don't know how it do? Help me!
 
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  • #2
Hint: you know that $\sqrt{x^2+y^2}$ is a norm, being the Euclidean metric. Proceed by using the relation below:
$$\sqrt{x^2+(2y)^2}=\sqrt{x^2+4y^2} = \|(x,y)\|$$
 
  • #3
Julio said:
If $\|(x,y)\|=\sqrt{x^2+4y^2}.$ Show that $\|.\|$ is an norm.

Hello MHB! :)

The cases $\|(x,y)\|\ge 0$ and $\|\lambda (x,y)\|=|\lambda|\|(x,y)\|$ are obvious, but the case $\|(x,y)+(x',y')\|\le \|(x,y)\|+\|(x',y')\|$ I don't know how it do? Help me!

If you can show instead that $\|(x,y)+(x^{\prime},y^{\prime})\|^{\color{red}2} \leq (\|(x,y)\|+\|(x^{\prime},y^{\prime})\|)^{\color{red}2}$, then it immediately follows that $\|(x,y)+(x^{\prime},y^{\prime})\| \leq \|(x,y)\|+\|(x^{\prime},y^{\prime})\|$. Do you know how to proceed from here?
 
  • #4
Bacterius said:
Hint: you know that $\sqrt{x^2+y^2}$ is a norm, being the Euclidean metric. Proceed by using the relation below:
$$\sqrt{x^2+(2y)^2}=\sqrt{x^2+4y^2} = \|(x,y)\|$$

Thanks Bacterius :)

But I don't understand which is the relation of the norm $\|.\|_2$ with the norm $\sqrt{x^2+4y^2}$ ?

Chris L T521 said:
If you can show instead that $\|(x,y)+(x^{\prime},y^{\prime})\|^{\color{red}2} \leq (\|(x,y)\|+\|(x^{\prime},y^{\prime})\|)^{\color{red}2}$, then it immediately follows that $\|(x,y)+(x^{\prime},y^{\prime})\| \leq \|(x,y)\|+\|(x^{\prime},y^{\prime})\|$. Do you know how to proceed from here?

Thanks Cris L T521.

Emm?, this it's good?

$\|(x,y)+(x',y')\|^2=\|(x+x',y+y')\|^2=\langle (x+x',y+y')\rangle =\langle (x,y)\rangle +\langle (x',y')\rangle +\langle (x',y)\rangle +\langle (x,y')\rangle=?$PD.: Sorry, my English is bad. :(
 
  • #5
Julio said:
Thanks Bacterius :)

But I don't understand which is the relation of the norm $\|.\|_2$ with the norm $\sqrt{x^2+4y^2}$ ?

Well, from the Euclidean metric we get that for all $(x, y)$ and $(x', y')$ we have:

$$\sqrt{(x + x')^2 + (y + y')^2} \leq \sqrt{x^2 + y^2} + \sqrt{x'^2 + y'^2}$$

So by direct substitution of $(u, 2v) \to (x, y)$ and $(u', 2v') \to (x', y')$ we have:

$$\sqrt{(u + u')^2 + (2v + 2v')^2} \leq \sqrt{u^2 + (2v)^2} + \sqrt{u'^2 + (2v')^2}$$

$$\sqrt{(u + u')^2 + (2(v + v'))^2} \leq \sqrt{u^2 + (2v)^2} + \sqrt{u'^2 + (2v')^2}$$

$$\sqrt{(u + u')^2 + 4(v + v')^2} \leq \sqrt{u^2 + 4v^2} + \sqrt{u'^2 + 4v'^2}$$

Which is what you wanted. Does that make sense?
 
  • #6
Julio said:
Thanks Cris L T521.

Emm?, this it's good?

$\|(x,y)+(x',y')\|^2=\|(x+x',y+y')\|^2=\langle (x+x',y+y')\rangle =\langle (x,y)\rangle +\langle (x',y')\rangle +\langle (x',y)\rangle +\langle (x,y')\rangle=?$PD.: Sorry, my English is bad. :(

Hm, it's not good. :-/

I would start off like this:

\[\begin{aligned}\|(x,y) + (x^{\prime},y^{\prime})\|^2 &= \langle (x,y) + (x^{\prime},y^{\prime}),(x,y) + (x^{\prime},y^{\prime})\rangle \\ &= \langle (x,y),(x,y)\rangle + \langle (x,y),(x^{\prime},y^{\prime})\rangle + \langle (x^{\prime},y^{\prime}), (x,y)\rangle + \langle (x^{\prime},y^{\prime}),(x^{\prime},y^{\prime})\rangle \\ &= \langle (x,y),(x,y)\rangle + 2 \langle (x,y),(x^{\prime},y^{\prime})\rangle + \langle (x^{\prime},y^{\prime}),(x^{\prime},y^{\prime})\rangle \\&= \|(x,y)\|^2 + 2\langle (x,y),(x^{\prime},y^{\prime})\rangle + \|(x^{\prime},y^{\prime})\|^2 \end{aligned}\]

To get the desired result, you'll need to recall that $\langle (x,y),(x^{\prime},y^{\prime})\rangle \leq \|(x,y)\|\|(x^{\prime},y^{\prime})\|$ by the Cauchy-Schwarz inequality.

Do you think you can wrap things up from here? (Bigsmile)
 

FAQ: Show $\|.\|$ is an Norm: Prove Triangle Inequality

1. What is a norm?

A norm is a mathematical concept that measures the size or magnitude of a vector in a vector space. It is similar to the concept of absolute value in one-dimensional space, but it can be extended to multiple dimensions.

2. Why is it important to prove the triangle inequality for a norm?

The triangle inequality is an essential property of a norm because it ensures that the distance between two points in a vector space is always greater than or equal to the sum of the distances between those points and a third point. This property is crucial for many applications in mathematics and science, such as optimization problems and distance-based algorithms.

3. How do you prove the triangle inequality for a norm?

The most common approach to proving the triangle inequality for a norm is by using the Cauchy-Schwarz inequality. This inequality states that the dot product of two vectors is always less than or equal to the product of their norms. By manipulating the dot product, we can obtain the triangle inequality.

4. Can the triangle inequality be extended to other mathematical concepts?

Yes, the triangle inequality can be extended to other mathematical concepts, such as metrics and inner products. In fact, many properties and theorems in mathematics are based on the triangle inequality, making it a fundamental concept in the field.

5. Are there any exceptions to the triangle inequality?

Yes, there are exceptions to the triangle inequality. These exceptions occur in non-Euclidean spaces, where the concept of distance is different from traditional vector spaces. For example, in the Pseudo-Euclidean geometry, the triangle inequality does not hold for certain vectors.

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