I have following Python code, a and b are lists(I know it isn't best way of getting intersections):
def get_intersection(a, b):
a = set(a)
b = set(b)
c = sorted(list(a&b))
return c
Let's call len(a) - m and len(b) - n, where is no additional information about a and b. Then time complexity of given code is O(m) + O(n) + O(m * n) + O((m + n) * log(m + n)).
I definitely can shorten O(m) and O(n), because they are much less than O(m * n), but what should I do with O((m + n) * log(m + n))?
How do i compare O(m * n) and O((m + n) * log(m + n))? Should I keep O((m + n) * log(m + n)) in final evaluation?
You can treat the total input size as n
; it doesn't really matter which argument contributes what to that total. (The two extremes are when one or the other argument is empty; moving items from one argument to the other doesn't change the overall amount of work you'll be doing.)
As such, both set(a)
and set(b)
are O(n) operations.
a & b
is also O(n); you don't need to compare every element of a
to every element of b
to compute the intersection, because sets are hash-based. You basically just make O(n) constant-time lookups. (I am ignoring the horrific corner case that makes set lookup linear. If you data has a hash function that doesn't map every item to the same value, you won't hit the worst case.)
sorted(a&b)
(no need to create a list first, but that's also just an O(n) operation) takes O(n lg n).
Because each of the preceding operations is performed in sequence, the total complexity of get_intersection
is O(n lg n).
I don't think that you can simplify the expression.
Indeed, if you set m
to a constant value, say 5
, you have a complexity
5n + (n+5)log(n+5) = O(n log(n))
and the first term is absorbed.
But if you set m = n
,
n² + 2n log(2n) = O(n²)
and this time the second term is absorbed. Hence all you can write is
O(mn + (m+n) log(m+n)).
With a change of variable such that s
is the sum and p
the product,
O(p + s log(s)).
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