? p-norm <= (p-1)norm <= <= 2-norm <= 1-norm?

  • Thread starter patiobarbecue
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In summary, the p-norm for a vector x in R^n is defined as || x ||_p = (x_1^p + x_2^p + ... + x_n^p)^{1/p}. The question is to verify if || x ||_p <= || x ||_{p-1}. It has been proven that for p = 2 and p = 3, || x ||_p is a true norm. It has also been shown by induction that for any n, n-1, || x ||_p is a true norm. However, it has been questioned if || x ||_p is a true norm for 0 < p < 1, as it satisfies the three
  • #1
patiobarbecue
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0
1.
the p-norm for a vector x in R^n is defined usually:

|| x ||_p = (x_1^p + x_2^p + ... + x_n^p)^{1/p}

the question is to verify:

|| x ||_p <= || x ||_{p-1}






Homework Equations



I guess even more generally p-norm is a decreasing function in p for "any" x?


The Attempt at a Solution



Neither Cauchy, nor the more general Holder doesn't seem to apply.
 
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  • #2
Try using Jensen's inequality:

If [itex]\phi[/itex] is a convex function, and [itex]y_1, \ldots, y_N[/itex] lie in its domain, and [itex]\lambda_1, \ldots \lambda_N[/itex] are positive real numbers such that

[tex]\sum_{n=1}^{N} \lambda_n = 1[/tex]

then

[tex]\phi\left(\sum_{n=1}^{N} a_n y_n\right) \leq \sum_{n=1}^{N} a_n \phi(y_n)[/tex]

For [itex]p = 2[/itex], try using [itex]y_n = x_n^2[/itex], [itex]\phi(x) = x^{1/2}[/itex], and [itex]a_n = 1/N[/itex]. You will have to modify these choices appropriately for general [itex]p[/itex].

Also, your proposed generalization is correct: if [itex]1 \leq q \leq p \leq \infty[/itex] then [itex]||x||_p \leq ||x||_q[/itex]. The [itex]p = \infty[/itex] case is handled separately (Jensen's inequality doesn't apply) but it's easy.
 
  • #3
I think it's a proof for norm 2 and 3. For any n, n-1 it's the same essentially.

First consider R2.
Note (x^2+y^2)^1/2 > (x^3+y^3)^1/3 is equivalent to
[1+(y/x)^2]^1/2 > [1+(y/x)^3]^1/3
Assume y/x < 1 without losing generality, then from [1 + ...] > 1 one sees
[1+(y/x)^2]^1/2 > [1+(y/x)^3]^1/2 > [1+(y/x)^3]^1/3.

Next do an induction toward Rn.
(x1^2+x2^2+...+xn^2)^1/2 > (x1^3+x2^3+...+xn^3)^1/3 is equivalent to
(y^2+xn^2)^1/2 > (z^3+xn^3)^1/3, where y and z stands for (x1^2+...+xn-1^2)^1/2 and (x1^3+...+xn-1^3)^1/3 respectively.
By induction y > z. Hence (y^2+xn^2)^1/2 > (z^2+xn^2)^1/2 > (z^3+xn^3)^1/3. QED.
 
  • #4
thanks, guys, it helps!
 
  • #5
||x||_p isn't a norm for p<1 ?

Hello people! I have a question about the p-norm.
We know that the p-norm is a true norm for p >= 1.
But I dont'n understand why it is not a true norm for 0 < p < 1.
I think it hold the three conditions for a norm include when 0 < p < 1.

Somebody can helpme??
Thanks in advance!
 

1. What is the definition of p-norm?

The p-norm is a mathematical concept used to measure the size of a vector or sequence. It is calculated by taking the pth root of the sum of the pth powers of the vector's components or sequence's terms.

2. How does p-norm relate to other norms?

The p-norm is a generalization of other norms, such as the 1-norm, 2-norm, and infinity norm. It can be thought of as a continuum between these norms, with the 1-norm at one end and the infinity norm at the other end.

3. What is the significance of (p-1)norm in relation to p-norm?

The (p-1)norm is important because it is the point at which the p-norm transitions from being a convex to a concave function. This transition indicates a change in the behavior of the p-norm and can have implications in optimization problems.

4. Can you give an example of when the 2-norm is equal to the 1-norm?

Yes, when the vector or sequence only contains positive values, the 2-norm and 1-norm will be the same. For example, the 2-norm of [3,4] and the 1-norm of [3,4] are both equal to 5.

5. How is the p-norm used in real-world applications?

The p-norm is commonly used in statistics, data analysis, and machine learning to measure the similarity or distance between data points. It is also used in optimization problems to find the minimum or maximum value of a function. In addition, the p-norm has applications in physics, engineering, and economics.

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