We use cookies to ensure you have the best browsing experience on our website. Please read our cookie policy for more information about how we use cookies.
Your solution is a bruteforce solution that checks each of the digits between the sets, therefore it is not efficient. The editorial shows a clever solution in O(sqrt(n)) solution.
For the runtime of your code, you have two generator comprehensions, where each comprehension checks through each element inthe length of the list for every i in the range.
If we were to compute what the runtime, it should be [(upper-lower) * (len(a) + len(b))] = O(n^2) #someone correct me if this is wrong
"For example, if the time required by an algorithm on all inputs of size n is at most 5n^3 + 3n for any n (bigger than some n_0), the asymptotic time complexity is O(n^3)."
Between Two Sets
You are viewing a single comment's thread. Return to all comments →
Your solution is a bruteforce solution that checks each of the digits between the sets, therefore it is not efficient. The editorial shows a clever solution in O(sqrt(n)) solution.
For the runtime of your code, you have two generator comprehensions, where each comprehension checks through each element inthe length of the list for every i in the range.
If we were to compute what the runtime, it should be [(upper-lower) * (len(a) + len(b))] = O(n^2) #someone correct me if this is wrong
for, for would be O(n^2) but we have for, for and for O(2n^2)
With time complexity we drop out the constant term.
https://en.wikipedia.org/wiki/Time_complexity
"For example, if the time required by an algorithm on all inputs of size n is at most 5n^3 + 3n for any n (bigger than some n_0), the asymptotic time complexity is O(n^3)."
... you're right^^