Start by clarifying the definition of H-Index and the input format (e.g., array of citation counts). Then present a solution that sorts the array and scans to find the maximum h where citations[h] >= h, or use a counting sort approach for O(n) time. Discuss trade-offs between sorting and counting sort, and handle edge cases like empty input or all zeros.
Pro tip: Mention that the H-Index can be computed in O(n) time using a counting array of size n+1, which is optimal and shows you understand bucket sort. Also, explicitly state the time and space complexity of each approach and when to prefer one over the other.
Confirm the definition: H-Index is the maximum h such that there are at least h papers with h or more citations. Ask about input constraints (e.g., size, citation range) and expected output.
Explain that sorting the citations in descending order allows a linear scan to find the first index where citations[i] < i+1, giving O(n log n) time. This is simple and often sufficient.
Propose an O(n) time and O(n) space solution using a frequency array of size n+1, where counts above n are capped at n. Then iterate from the highest citation count downward, accumulating papers until the count meets or exceeds the current index.
Compare the sorting approach (O(n log n) time, O(1) extra space if in-place) with counting sort (O(n) time, O(n) space). Discuss when each is preferable based on input size and memory constraints.
Walk through examples like [3,0,6,1,5] (H-Index 3) and [0] (H-Index 0). Mention edge cases: empty array, all zeros, all large numbers, and duplicates.
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