When is the Cauchy-Schwartz inequality as large as possible?

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The Cauchy-Schwartz inequality [itex](\sum_{i=1}^n x_i^2)(\sum_{i=1}^n y_i^2) - (\sum_{i=1}^n x_iy_i)^2 \geq 0[/itex] holds with equality (or is as "small" as possible) if there exists an [itex]a \gt 0[/itex] such that [itex]x_i=ay_i[/itex] for all [itex]i=1,...,n[/itex].

But when is the inequality as "large" as possible? That is, can we say anything under what conditions [itex](\sum_{i=1}^n x_i^2)(\sum_{i=1}^n y_i^2) - (\sum_{i=1}^n x_iy_i)^2[/itex] is as large as possible?
 
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That's easy ! If all xy are negative. Can you interpret that ?
Oops. Major glitch trying to reply too hasty.
First of all welcome to PF :smile: ! And I'll be back soon to post something more useful -- if I don't get corrected sooner by someone more awake ...
 
Take 2: That's easy ! If all xiyi are zero. Can you interpret that ?
 
Sorry, I forgot to say that each [itex]x_i \gt 0[/itex] and [itex]y_i \gt 0[/itex]
 
I remember Cauchy-Schwartz had something to do with vector products and you look at ##( \vec x + \vec y ) ^2 = \vec x^2 + \vec y ^2 + 2 \vec x \cdot \vec y = \vec x^2 + \vec y ^2 + 2 \,| \vec x | \, |\vec y| \cos \alpha##.

The equals sign in CS is if ##|\cos\alpha| = 1 ## so I have a feeling that what you asked for might be found at ##\cos\alpha= 0 ##.

In post #4 you exclude =0 but then your extreme can be approached as close as desired by letting ##\cos\alpha \rightarrow 0 ##.

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If you write the summations as dot products, you can readily see the answer. Let [itex]\vec{x} = \langle x_1, x_2, \dotsm, x_n \rangle[/itex] and [itex]\vec{y} = \langle y_1, y_2, \dotsm, y_n \rangle[/itex]. Then the Cauchy-Schwartz inequality can be restated as:

[itex]\underbrace{(\vec{x} \cdot \vec{x})}_{\text{dot product}} \cdot<br /> \underbrace{(\vec{y}\cdot \vec{y} )}_{\text{dot product}} \ge \underbrace{\vec{x} \cdot \vec{y}}_{\text{dot product}}[/itex] or equivalently [itex]\| \vec{x} \|^2 \cdot \| \vec{y} \|^2 \ge \| \vec{x} \| \cdot \| \vec{y} \| \cdot \cos(\theta)[/itex]

where [itex]\theta[/itex] is the angle between [itex]\vec{x}[/itex] and [itex]\vec{y}[/itex]. Since [itex]\vec{x}\cdot \vec{y} = 0[/itex] if the two vectors are orthogonal, that's where you will get the largest value.
 
It can be arbitrarily large. Just take any xi:s and yi:s for which the difference is > 0 and multiply all the xi:s with an arbitrary lange t > 0.