Analyzing time complexity?

O(m) + O(n) = O(m+n), if you know that m>n then O(m+n)=O(m+m)=O(m).

O(m) + O(n) = O(m+n), if you know that m>n then O(m+n)=O(m+m)=O(m). Note: hashes theoretically don't guarantee O(1) lookup, but practically you can count on it (= it's the average complexity, the expected runtime for a random input). Also note, that your algo will repeatedly signal duplicated elements of be which are also present in a.

If this is a problem you have to store in the hash that you already checked/printed out that element.

Average case time complexity is O(m + n). This is what you should consider if you are doing some implementation, since hash maps would usually not have collisions. O(m+n) = O(max(m, n)) However, if this is an test question, by time complexity, people mean worst case time complexity.

Worst case time complexity is O(mn) since each of second steps can take O(n) time in worst case.

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