← Bytedance Interview Insights
Start by clarifying the problem and constraints, then present a dynamic programming solution with O(n^2) time, followed by an optimized O(n log n) approach using binary search. Explain the intuition behind each and discuss trade-offs.
Pro tip: Mention that the O(n log n) solution uses patience sorting and that the tails array is not the actual LIS but its length is correct. This shows deep understanding and avoids a common misconception.
Confirm that the subsequence must be strictly increasing and that elements are not necessarily contiguous. Ask about input size to determine the expected time complexity.
Explain the O(n^2) dynamic programming approach where dp[i] is the length of the longest increasing subsequence ending at index i. Recurrence: dp[i] = 1 + max(dp[j] for j < i and nums[j] < nums[i]).
Introduce the O(n log n) approach using a tails array and binary search. For each number, find the first element in tails that is >= it and replace it; if none, append. The length of tails is the answer.
State time and space complexity for both approaches. Discuss edge cases: empty array, all equal elements, strictly decreasing array, and large input.
Write clean code for the chosen approach, preferably the optimized one. Walk through a small example to verify correctness and handle edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.