← Bytedance Interview Insights
I went with the min-heap approach first because it felt safer to explain, but they pushed back and asked if I could do better on average time complexity.
Start by clarifying the problem constraints (e.g., array size, value range, duplicates) and then present multiple solutions with trade-offs: sorting, min-heap, and quickselect. Emphasize the optimal average-case O(n) quickselect approach, but also mention the heap-based O(n log k) solution as a robust alternative.
Pro tip: Discuss how to handle duplicates and edge cases (e.g., k=1, k=n, empty array) and mention that quickselect's worst-case can be avoided with randomized pivot selection or median-of-medians. This shows depth and practical awareness.
Ask about input size, value range, duplicates, and whether the array can be modified. This helps determine the most suitable algorithm.
Mention sorting (O(n log n)) as a baseline, then introduce heap-based (O(n log k)) and quickselect (average O(n)) as improvements.
Describe quickselect: partition the array around a pivot, then recurse on the side containing the kth largest. Highlight average O(n) time and O(1) space.
Discuss handling duplicates, randomized pivot to avoid worst-case O(n^2), and iterative vs recursive implementation.
Compare time and space complexity of each approach, and explain when to prefer heap (e.g., streaming data) vs quickselect (in-memory, average-case performance).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.