I went straight for the brute force O(n^3) approach first which, in hindsight, was probably not the move for a TikTok screen.
Start by clarifying the problem constraints (e.g., array size, duplicates, sorted input) and then present an efficient solution. The optimal approach is to sort the array and use a two-pointer technique for each element, achieving O(n^2) time and O(1) extra space (or O(n) if sorting is not allowed).
Pro tip: Mention that you would handle duplicates to avoid returning duplicate triplets, and discuss trade-offs between sorting and hashing approaches. This shows attention to edge cases and practical implementation details.
Ask about input size, whether the array can contain duplicates, if the array is sorted, and whether we need to return all triplets or just one. Also confirm if the solution should be in-place or if extra space is allowed.
Decide between sorting + two-pointer (O(n^2) time, O(1) space) or hashing (O(n^2) time, O(n) space). Explain why sorting is often preferred for its simplicity and lower space complexity.
For sorting approach: sort the array, then for each index i, use two pointers (left = i+1, right = n-1) to find pairs that sum to target - arr[i]. Skip duplicates to avoid duplicate triplets.
State time complexity O(n^2) and space complexity O(1) (excluding sorting). Discuss edge cases: fewer than 3 elements, no solution, multiple solutions, and duplicate handling.
Walk through a small example (e.g., array [-1,0,1,2,-1,-4], target 0) to demonstrate correctness and duplicate skipping.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.