# Running Time Analysis of a Simple Binary Search Algorithm

I'm stuck with the following problem by Skiena (The Algorithm Design Manual, p. 106):

Problem: Give an efficient algorithm to determine whether two sets (of size $$m$$ and $$n$$, respectively) are disjoint. Analyze the worst-case complexity in terms of $$m$$ and $$n$$, considering the case where $$m$$ is substantially smaller than $$n$$.

Solution: First sort the big set – The big set can be sorted in $$O(n\log n)$$ time. We can now do a binary search with each of the m elements in the second, looking to see if it exists in the big set. The total time will be $$O((n + m)\log n)$$.

My question: Why is the running time $$O((n + m)\log n)$$? I would have to perform m binary searches in total (one binary search for every element in $$m$$) - as one binary search has a running time of $$O(\log n)$$, I would have to perform $$m \cdot \log n$$ operations in the worst case. How does this - if at all - translate into $$O((n + m)\log n)$$?

• It takes $O(n\log n)$ to sort the bigger set, and to $O(m\log n)$ to do the binary search, for a total of $O(n\log n + m\log n) = O((m+n)\log n)$. Note that you can do even better if you sort the smaller set instead, replacing $\log n$ with $\log m$. May 27, 2020 at 18:07
• Thanks, I got it know! May 28, 2020 at 9:44

Sorting the big set takes time $$O(n\log n)$$. You perform $$m$$ binary searches, each taking $$O(\log n)$$, for a total of $$O(m\log n)$$ time spent on binary search. The total running time of the algorithm is thus $$O(n\log n + m\log n) = O((n + m)\log n) = O(n\log n),$$ assuming $$m \leq n$$.
Note that it is even better to sort the smaller list, since then the running time improves to $$O(n\log m)$$.