Start by clarifying the problem constraints (e.g., array size, value range, duplicates) and then present a solution using a min-heap of size k, which efficiently finds the k-th largest element in O(n log k) time. Alternatively, mention the Quickselect algorithm for average O(n) time, but be prepared to discuss its worst-case O(n^2) and how to mitigate it with random pivoting.
Pro tip: Meta interviewers value practical trade-offs: explicitly compare heap vs. Quickselect in terms of time, space, and worst-case behavior, and mention that for streaming data, a heap is preferred. Also, always state the time complexity clearly and justify it.
Ask about constraints: array size, possible duplicates, whether k is always valid, and if the array can be modified. This shows attention to detail and helps choose the right approach.
Describe the min-heap approach: iterate through the array, maintain a min-heap of size k, and after processing, the root is the k-th largest. Alternatively, mention Quickselect for better average time.
State the time complexity: O(n log k) for heap, O(n) average for Quickselect, and O(n^2) worst-case for Quickselect. Also mention space complexity: O(k) for heap, O(1) for in-place Quickselect.
Compare approaches: heap is simpler, handles streaming, and has guaranteed O(n log k); Quickselect is faster on average but has poor worst-case unless randomized. Mention edge cases like k=1, k=n, or empty array.
Write clean code for the chosen approach, then walk through a small example to verify correctness. Be prepared to optimize or switch approaches if the interviewer asks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.