i'm not sure if this should be obvious, but it surprised me to find that both are equal to πpic.twitter.com/973xR17ilx
PhD student of Theoretical Particle Physics @UCIrvine l @NSF Fellow l Physics & Math Animations l Patreon: https://www.patreon.com/inertialobserver …
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i'm not sure if this should be obvious, but it surprised me to find that both are equal to πpic.twitter.com/973xR17ilx
Plot of the error between the integral and the sum as a function of the limits of integration/sum.pic.twitter.com/jP9zq7vr6u
This feels strictly... not true? The sum of a function over a domain only approaches its Riemann sum when the rectangle widths vanish relative to the characteristic scale on which the function varies. This simply doesn't happen here. Unless I'm missing something..?
i was struggling with that too, but thought maybe i was mistaken.. What's your reasoning?
I think this simply doesn't apply here. The sinc function oscillates with the same frequency on its whole domain. In no way does its discrete sum approximate its integral, except for the fact that they happen to be equal.
The approximation gets better the further you go out, though. I *think* this is because the peaks and troughs then tend to just their heights cause ω-> infinity
I mean, any explanation of this needs to explain how the errors for small x get exactly compensated. Arguing about the large x behavior here isn't going to tell you about why the sum and integral exactly match.pic.twitter.com/OGTE7eTxeW
It is *very* interesting to note that when you actually properly take the limit as the rectangles get smaller, the result is insensitive to the size of the rectangles (so long as they don't get too big).
that's true.. I suppose one still needs to explain why all of the finite bits exactly match up to give the exact same answer also
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