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Knuth originally defining them https://dl.acm.org/doi/10.1145/1008328.1008329
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f(n) = O(g(n)) means as n increases, f(n)/g(n) must not grow beyond a constant

f(n) = o(g(n)) means as n increases, f(n)/g(n) must shrink

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Nitpick:

s/must shrink/must eventually shrink, after finitely many n/

Equivalently, must go to zero in the limit.

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depends on what you think shrink means. If you mean decreasing, that's false even eventually.
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Big O basically drops constants

O(2n+1) = O(2n) = O(n)

o(2n+1) > o(2n) > o(n)

Both define upper boundary, but big O can read like "the function is generally proportional to ___" and little o can read like "the function is never more than _____"

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that's not the convention I know... (from CLRS algorithms book).

Big O is sort of like <=. little o is sort of like <.

https://dl.acm.org/doi/10.1145/1008328.1008329

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yes, GP is slightly off, but the idea of <= and < is accurate.

O(n) is: "there exists a K such that, for sufficiently large n, K*n is always larger".

o(n) is: "for every K, for sufficiently large n, K*n is always larger". (So o(2n+1) and o(2n) and o(n) are the same thing.)

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If f is O(g) and o(h) then

  Lim f/g -> C a constant
And

  Lim f/h -> 0
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> Lim f/g -> C a constant.

This isn't quite correct. O(g) doesn't require the limit to exist.

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Ah, true, I was being sloppy. Thanks
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Lmao! If someone is asking the difference between big and little o notation this is absolutely not the right way to clarify it.

If you were joking then maybe you got me

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I do hope you can find and reattach your ass; that does sound painful.
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Both are upper bounds. But small o is strictly smaller than upper bound. Big O is not greater than upper bound.

https://stackoverflow.com/questions/1364444/difference-betwe...

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