For some value of "faster" (where faster is not better), an O(N2)-bounded algorithm grows faster than an O(N)-bounded one.
This shows you don't know much about big-Oh.

Any algorithm whose running time is in "O(N)" (for N towards infinity) is also in "O(N2)".

And big-Oh notation is not "woolly". In fact, if the running time of an algorithm is expressed as T(N), with N the size of the input, and it's said that T(N) is in O(f(N)), it means that: 

    ∃ M, c > 0: ∀ m > M: T(m) <= c f(m)
The difference between my code and your code is that, as the data set gets larger and the problem set more complex, my editing may go a bit more smoothly.
And you base this statement on what? (And what has editing to do with the size of the data set?)
To put it another way, I would fear to maintain your code, while you would only hate to maintain mine.
I don't know what to think of a programmer that fears to maintain code that consists of two small, self contained blocks, and a small subroutine.

In reply to Re^3: Gay Dating; or, Thrifty Set Intersections with an Accumulator by JavaFan
in thread Gay Dating; or, Thrifty Set Intersections with an Accumulator by Xiong

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