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

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In summary: 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".
  • #1
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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  • #2
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##.
 
  • #3
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?
 
  • #4
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.
 
  • #5
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?
 
  • #6
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.
 

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

What is the difference between 1-norm and 2-norm for vectors?

The 1-norm and 2-norm are both ways of measuring the magnitude or length of a vector. The 1-norm, also known as the Manhattan norm, is calculated by adding up the absolute values of each element in the vector. The 2-norm, also known as the Euclidean norm, is calculated by taking the square root of the sum of the squares of each element in the vector. In other words, the 1-norm measures the total distance between the vector's origin and its endpoint, while the 2-norm measures the straight-line distance between these points.

Why is it important to prove that the 1-norm is greater than or equal to the 2-norm?

Proving that the 1-norm is greater than or equal to the 2-norm is important because it confirms that the 1-norm is a valid way of measuring the magnitude of a vector. This is especially useful in fields such as engineering and physics, where vectors are commonly used to represent physical quantities. The proof also helps us understand the relationship between different norms and how they can be used in different situations.

How is the proof for the 1-norm being greater than or equal to the 2-norm carried out?

The proof for the 1-norm being greater than or equal to the 2-norm is typically done using mathematical induction. This involves showing that the statement is true for a base case, and then showing that it holds for any arbitrary case. The proof relies on properties of absolute values and inequalities to show that the 1-norm is always greater than or equal to the 2-norm.

Can the proof for the 1-norm being greater than or equal to the 2-norm be applied to other norms?

No, the proof for the 1-norm being greater than or equal to the 2-norm only applies to these two specific norms. Each norm has its own unique properties and relationships with other norms, so the proof for one norm cannot be applied to another. However, the general concept of proving one norm is greater than or equal to another can be applied to other pairs of norms.

What are some practical applications of the 1-norm and 2-norm for vectors?

The 1-norm and 2-norm have various practical applications in fields such as data analysis, signal processing, and optimization. In data analysis, the 1-norm is commonly used for feature selection, while the 2-norm is used for regularization. In signal processing, the 2-norm is used to measure signal-to-noise ratio. In optimization, the 1-norm and 2-norm are used to define different types of convex functions, which are important in optimization algorithms.

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