It is not my domain of expertise, but assuming I am not wrong, maybe it's worth adding something like this: Big O measures the upper bound on a function. Big O can, but does not necessarily, refer to the running time in the worst case. We can determine Big O for various scenarios, including the best case, worst case, typical case, etc.
Update: I re-read the part of the sentence about this in the article:
Big O notation gives us our algorithm's approximate run time in the worst case, or in other words, its upper bound
I think @vickylai meant that the function would not have worse performance than the given upper bound, so it was a "worst case" in that sense.
However the input was not specified. For example, if we have a (contrived) function that is O( n2 ) for any even number as input, but O( n ) for odd-numbered input, the "worst case" in that sense is when it receives an even number and the "best case" is if receives an odd number.
The use of the term "worst case" refers to two different things in each of the two paragraphs above.
It doesn't always make a difference, but in practice we can't study the big O of any algorithm without some context around input. It's worth keeping that in mind.
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It is not my domain of expertise, but assuming I am not wrong, maybe it's worth adding something like this: Big O measures the upper bound on a function. Big O can, but does not necessarily, refer to the running time in the worst case. We can determine Big O for various scenarios, including the best case, worst case, typical case, etc.
Update: I re-read the part of the sentence about this in the article:
I think @vickylai meant that the function would not have worse performance than the given upper bound, so it was a "worst case" in that sense.
However the input was not specified. For example, if we have a (contrived) function that is O( n2 ) for any even number as input, but O( n ) for odd-numbered input, the "worst case" in that sense is when it receives an even number and the "best case" is if receives an odd number.
The use of the term "worst case" refers to two different things in each of the two paragraphs above.
It doesn't always make a difference, but in practice we can't study the big O of any algorithm without some context around input. It's worth keeping that in mind.