Prove 1-norm is => 2-norm for vectors

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Homework Help Overview

The discussion revolves around proving the inequality between the 1-norm and the 2-norm of vectors, specifically that the 1-norm is greater than or equal to the 2-norm. The subject area is vector norms in linear algebra.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants express uncertainty about the proof's formality and whether the reasoning they have is sufficient. There is a suggestion to compare the squares of the norms as a potential approach to the proof.

Discussion Status

Some participants have offered insights into the reasoning behind the comparison of squared norms, while others are questioning the completeness of the proof and what constitutes a valid argument. The discussion is ongoing with various interpretations being explored.

Contextual Notes

Participants note the challenge of formalizing their reasoning and the need to clarify assumptions regarding the properties of sums and squares in their arguments.

charlies1902
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Homework Statement


Show that ##||x||_1\geq ||x||_2##


2. Homework Equations

##||x||_1 = \sum_{i=1}^n |x_i|##
##||x||_2 = (\sum_{i=1}^n |x_i|^2)^.5##

The Attempt at a Solution


I am having a hard time with this, because the question just seems so trivial, that I don't even know how to prove it. By looking at the relevant equations we can easily see that the 1-norm is the sum of the absolute value of each element of x-vector. We also see that the 2-norm is the square root of the sum of squares of each absolute value of each element in x. Just by looking at the definition you can easily see that the 1-norm is always greater or equal to the 2-norm.

is there an actual formal way to do this?
It really just seems trivial to me.
 
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charlies1902 said:

Homework Statement


Show that ##||x||_1\geq ||x||_2##


2. Homework Equations

##||x||_1 = \sum_{i=1}^n |x_i|##
##||x||_2 = (\sum_{i=1}^n |x_i|^2)^.5##

The Attempt at a Solution


I am having a hard time with this, because the question just seems so trivial, that I don't even know how to prove it. By looking at the relevant equations we can easily see that the 1-norm is the sum of the absolute value of each element of x-vector. We also see that the 2-norm is the square root of the sum of squares of each absolute value of each element in x. Just by looking at the definition you can easily see that the 1-norm is always greater or equal to the 2-norm.

is there an actual formal way to do this?
It really just seems trivial to me.
Compare ##(||x||_1)^2## and ##(||x||_2)^2##.
 
Ray Vickson said:
Compare ##(||x||_1)^2## and ##(||x||_2)^2##.
Thanks. But isn't that just a different way of saying what I said above?
 
charlies1902 said:
Thanks. But isn't that just a different way of saying what I said above?

No. It constitutes a proof, rather than just a claim.
 
Ray Vickson said:
No. It constitutes a proof, rather than just a claim.
Hmmm. I might be missing something here:
So if I square both sides I obtain:
##||x||_1= (\sum |x_i|)^2##
and
##||x||_2=(\sum |x_i|^2)##

Since, the square of sums of absolute values is always greater or equal to the sum of squares, we have proved that ##||x||_1 \geq ||x||_2##.

Is what I just stated considered a proof or claim?
 
charlies1902 said:
Hmmm. I might be missing something here:
So if I square both sides I obtain:
##||x||_1= (\sum |x_i|)^2##
and
##||x||_2=(\sum |x_i|^2)##

Since, the square of sums of absolute values is always greater or equal to the sum of squares, we have proved that ##||x||_1 \geq ||x||_2##.

Is what I just stated considered a proof or claim?

Now it would be a proof if you explained why "square of sums of absolute values is always greater or equal to the sum of squares".

It has to do, of course, with the presence of (non-negative) cross-product terms of the form ##2 |x_1| \cdot |x_j|## in one expansion but not the other.
 

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