Proving the Inequality between Lp Norms: A Demonstration for Homework

So:##|x_1+y_1+x_2+y_2+...+x_n+y_n| \leq \sqrt{n} \sqrt{\left ( |x_1|^2+|x_2|^2+...+|x_n|^2 \right )}##Which is what I needed to proof. Thank you very much for all your help.
  • #1
Telemachus
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Homework Statement


Hi there. I have to prove this inequality:

##||x||_2 \leq ||x||_1 \leq \sqrt{n} ||x||_2##

Where ##||x||_2## is the ##l_p## norm with p=2, so that:

##||x||_2=(|x_1|^2+|x_2|^2+...+|x_n|^2)^{\frac{1}{2}}##

And similarly ##||x||_1=|x_1|+|x_2|+...+|x_n|## is the ##l_1## vectorial norm.

so, the first part I think its easy (I suspect the second part is also easy, but I couldn't get through it).

I have that:

##||x||_2=(|x_1|^2+|x_2|^2+...+|x_n|^2)^{\frac{1}{2}}\leq |x_1|+|x_2|+...+|x_n|=||x||_1## which is directly satisfied by applying the triangle inequality. So I think that's done.

Now, for the other inequality I have to show that: ##||x||_1 \leq \sqrt{n} ||x||_2##

I couldn't find the way to show that, so I thought that perhaps someone here could help me.

Thanks in advance.
 
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  • #2
Telemachus said:

Homework Statement


Hi there. I have to prove this inequality:

##||x||_2 \leq ||x||_1 \leq \sqrt{n} ||x||_2##

Where ##||x||_2## is the ##l_p## norm with p=2, so that:

##||x||_2=(|x_1|^2+|x_2|^2+...+|x_n|^2)^{\frac{1}{2}}##

And similarly ##||x||_1=|x_1|+|x_2|+...+|x_n|## is the ##l_1## vectorial norm.

so, the first part I think its easy (I suspect the second part is also easy, but I couldn't get through it).

I have that:

##||x||_2=(|x_1|^2+|x_2|^2+...+|x_n|^2)^{\frac{1}{2}}\leq |x_1|+|x_2|+...+|x_n|=||x||_1## which is directly satisfied by applying the triangle inequality. So I think that's done.

Now, for the other inequality I have to show that: ##||x||_1 \leq \sqrt{n} ||x||_2##

I couldn't find the way to show that, so I thought that perhaps someone here could help me.

Thanks in advance.

Not sure what you mean by follows "directly" from the triangle inequality. I would suggest looking at ##n=2## for ideas. Then it says$$
\sqrt{a^2+b^2}\le |a| + |b| \le \sqrt 2\sqrt{a^2+b^2}$$Square all three sides and see if that gives you any ideas.
 
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  • #3
I mean that this follows by the triangle inequality: ##\sqrt{a^2+b^2}\leq |a|+|b|##. But what about this: ##|a|+|b|\leq \sqrt{2} \sqrt{a^2+b^2}##? how do I prove that's true? and how do I do that for arbitrary n?

By squaring all sides I get: ##0 \leq 2|ab| \leq a^2+b^2##
 
  • #4
I think I see what you meant. I'll try and tell you. Thanks.
 
  • #5
Telemachus said:
I mean that this follows by the triangle inequality: ##\sqrt{a^2+b^2}\leq |a|+|b|##. But what about this: ##|a|+|b|\leq \sqrt{2} \sqrt{a^2+b^2}##? how do I prove that's true? and how do I do that for arbitrary n?

By squaring all sides I get: ##0 \leq 2|ab| \leq a^2+b^2##

So, in general, you need to show that if ##I_n = \{ (i,j): 1 \leq i < j \leq n \}## then
[tex] 2 \sum_{I_n} |a_i| | a_j| \leq (n-1) \sum a_i^2 [/tex]
The case for ##n = 2## is easy: ##0 \leq (|a|-|b|)^2 = a^2 +b^2 - 2 |a| |b|##.
 
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  • #6
@Telemachus: Not knowing your background, I wonder if you already have the Cauchy-Schwartz inequality or not:$$
|(\vec x, \vec y)|\le \parallel \vec x \parallel \parallel \vec y \parallel$$where the left side is just the dot product in ##\mathbb R^n## and those are ##l_2## norms on the right. One proof of your right hand inequality is often demonstrated using it.
 
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  • #7
Yes, I've tried to proove it by using Cauchy Schwartz inequality, but I coulnd't get it. I'll show you:

##| \sum_i x_i y_i | \leq ||x||_2 ||y||_2##

So I have: ##|x_1 y_1+x_2 y_2+...+x_n y_n | \leq \sqrt{ \left ( |x_1|^2+|x_2|^2+...+|x_n|^2 \right )\left ( |y_1|^2+|y_2|^2+...+|y_n|^2 \right ) }##

What I've tried (which is probably not in the right way) was just setting all ##y_i=1 \forall i##. And then I get:

##|x_1+x_2 +...+x_n| \leq \sqrt{n} \sqrt{\left ( |x_1|^2+|x_2|^2+...+|x_n|^2 \right )}=\sqrt{n} ||x||_2##

Which looks close, but I aslo have this:

##|x_1+x_2 +...+x_n| \leq |x_1|+|x_2| +...+|x_n|=||x||_1##

So its still inconclusive.
 
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  • #8
Telemachus said:
Yes, I've tried to proove it by using Cauchy Schwartz inequality, but I coulnd't get it. I'll show you:

##| \sum_i x_i y_i | \leq ||x||_2 ||y||_2##

So I have: ##|x_1 y_1+x_2 y_2+...+x_n y_n | \leq \sqrt{ \left ( |x_1|^2+|x_2|^2+...+|x_n|^2 \right )\left ( |y_1|^2+|y_2|^2+...+|y_n|^2 \right ) }##

What I've tried (which is probably not in the right way) was just setting all ##y_i=1 \forall i##. And then I get:

##|x_1+x_2 +...+x_n| \leq \sqrt{n} \sqrt{\left ( |x_1|^2+|x_2|^2+...+|x_n|^2 \right )}=\sqrt{n} ||x||_2##

Which looks close, but I aslo have this:

##|x_1+x_2 +...+x_n| \leq |x_1|+|x_2| +...+|x_n|=||x||_1##

So its still inconclusive.
You are almost there.

Cauchy Schwarz is valid for any choice of ##x_i, y_i##.
You chose ##y_i=1##. Why not also make a specific choice for ##x_i##? (Imagine for a moment that all ##x_i \geq 0##.)
 
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  • #9
What if your ##\vec x## vector is ##\langle |x_1|,|x_2|,...|x_n|\rangle## in the first place?
 
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  • #10
Done :D thanks.
 

1. What is the Lp norm?

The Lp norm is a mathematical concept used to measure the size or magnitude of a vector in a certain space. It is also known as the p-norm or the Lp distance. The value of p determines the type of norm, with p=1 being the Manhattan norm, p=2 being the Euclidean norm, and p=∞ being the maximum norm.

2. How is the Lp norm calculated?

The Lp norm is calculated by taking the p-th root of the sum of the absolute values of the vector's elements raised to the power of p. Mathematically, it can be represented as ||x||p = (|x1|p + |x2|p + ... + |xn|p)1/p.

3. What is the significance of the Lp norm?

The Lp norm has various applications in mathematics, statistics, and computer science. It is commonly used to measure the distance or similarity between two vectors, and is also used in optimization and machine learning algorithms.

4. How is the Lp norm different from other norms?

The Lp norm is different from other norms, such as the L1, L2, and L∞ norms, in terms of the type of distance or magnitude it measures. For example, the L1 norm measures the distance between two points in a straight line, while the L2 norm measures the shortest distance between two points. The L∞ norm, on the other hand, measures the maximum difference between two points.

5. Can the Lp norm be extended to higher dimensions?

Yes, the Lp norm can be extended to higher dimensions, such as 3D or n-dimensional space. The formula for calculating the Lp norm remains the same, but with the addition of more terms to account for the extra dimensions. This allows for the measurement of distance or magnitude in more complex vector spaces.

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