Why is Simple Inequality True?

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The discussion centers on the validity of the inequality involving the sums of absolute differences and their squares, which is a specific case of the Cauchy–Schwarz inequality. Participants highlight that this can be proven using the generalized mean inequality, known as the AM-QM inequality, which states that the arithmetic mean is less than or equal to the quadratic mean for non-negative numbers. Alternative proof methods include rearrangement techniques and geometric reasoning related to vector lengths. The conversation also touches on the existence of multiple insightful proofs for this inequality. Overall, the thread provides a thorough exploration of the mathematical principles underlying the inequality.
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Can anybody tell me why

\sum_{j=1}^p |x_j-y_j| \leq \left( \sum_{j=1}^p 1\right)^{1/2} \left( \sum_{j=1}^p |x_j-y_j|^2 \right)^{1/2}

is true?

Thank you!
 
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This is a special case of the Cauchy–Schwarz inequality which in the finite-dimensional real case state:
\sum_{i=1}^n a_i b_i \leq \left( \sum_{i=1}^n a_i^2 \right)^{1/2} \left( \sum_{i=1}^n b_i^2 \right)^{1/2}
For all real numbers a_i, b_i.
 
Oh, thank you very much :)

Just as a little side-Q: Since this is a special case, as you say, are there also some more insightful proofs? Or is it too abstract for that?
 
There are plenty of ways to show it.

For simplicity let z_n = |x_n-y_n| (no need to keep track of both x_j and y_j).

Method 1 (means):
The generalized mean inequality says that:
M_p(a_1,a_2,\ldots,a_n) = \left(\frac{a_1^p + a_2^p + \cdots + a_n^p}{n}\right)^{1/p}
Is increasing as a function of p if a_1, ..., a_n are non-negative numbers. In particular:

M_1(z_j) =\frac{\sum_{j=1}^p z_j }{p} \leq \left(\frac{\sum_{j=1}^p z_j^2}{p}\right)^{1/2} = M_2(z_j)
(this is often referred to as the AM-QM inequality since M_1 is called the Arithmetic Mean and M_2 is called the Quadratic Mean).
[EDIT: Note also that the AM-QM inequality is practically the same as yours so if you want more proofs just search for proofs of the AM-QM inequality]Method 2 (rearrangement and fiddling with indices):
Assume without loss of generality z_1 \leq z_2 \leq \cdots \leq z_p (otherwise just rearrange the sequence). You wish to show:
\left(\sum_{j=1}^p z_j \right)^2 \leq p \left( \sum_{j=1}^p z_j^2 \right)
We can extend the sequence z_n to all integer indices by letting z_{np+k} = z_k for all integers n and k (in other words we just extend it by letting z_{p+1} = z_1, z_{p+2} = z_2, ...).

You have
\left(\sum_{j=1}^p z_j \right)^2 = \sum_{i=1}^p \sum_{j=1}^{p} z_i z_j = \sum_{i=1}^p \sum_{j=1}^{p} z_jz_{j+i}

By the rearrangement inequality:
\sum_{j=1}^{p} z_jz_{j+i} \leq \sum_{j=1}^{p} z_j^2
So:
\sum_{i=1}^p \sum_{j=1}^{p} z_jz_{j+i} \leq \sum_{i=1}^p\sum_{j=1}^{p} z_j^2 = p\sum_{j=1}^{p} z_j^2I also guess there is a way to reason geometrically since:
\left(\sum_{j=1}^p z_j^2\right)^{1/2}
is the length of the vector (z_1,z_2,...,z_j).
 
Last edited:
My apologies for not having replied sooner! I have read this much earlier, but just remembered I had forgotten to reply!

Thank you very much, it's rare to get such a helpful post, was exactly what I was looking for :)
 
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