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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Bytedance SET interview that leaned harder on algorithms than I expected. The palindrome substring problem sounds like a warmup but they pushed me through multiple solution tiers, so come prepared to actually discuss tradeoffs not just code one approach.

Questions Asked (1)

Q1

Given a string, find the longest palindromic substring and walk through multiple approaches including a quadratic solution, dynamic programming, and a linear time algorithm.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Started with expand-around-center which felt safe, but then they asked me to keep going.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then present the brute-force O(n^3) approach as a baseline. Progress to the expand-around-center O(n^2) method, followed by dynamic programming O(n^2) with O(n^2) space, and finally explain Manacher's algorithm for O(n) time. Compare trade-offs and discuss when each is appropriate.

Pro tip: Emphasize that while Manacher's is optimal, the expand-around-center approach is often preferred in practice due to simplicity and low constant factors. Mention that DP uses extra space and may not be ideal for very long strings.

1. Clarify requirements

Ask about input size, character set, and whether multiple palindromes of same length need handling. Confirm expected time/space constraints.

2. Present brute-force and expand-around-center

Describe O(n^3) brute-force checking all substrings. Then explain O(n^2) expand-around-center: for each center (2n-1 centers), expand while characters match.

3. Explain dynamic programming

Define dp[i][j] as whether substring i..j is palindrome. Recurrence: dp[i][j] = (s[i]==s[j] && dp[i+1][j-1]). Track longest. O(n^2) time and space.

4. Introduce Manacher's algorithm

Explain linear time O(n) algorithm using transformed string with separators, maintaining center and right boundary to avoid redundant expansions. Compute radii and extract longest palindrome.

5. Compare trade-offs and conclude

Discuss time/space complexity, implementation complexity, and practical considerations. Recommend expand-around-center for most interviews unless O(n) explicitly required.

Key Points to Mention

  • Time and space complexity of each approach: O(n^3), O(n^2) time O(1) space, O(n^2) time O(n^2) space, O(n) time O(n) space.
  • Expand-around-center handles both odd and even length palindromes by considering 2n-1 centers.
  • Dynamic programming recurrence and initialization for single characters and two-character palindromes.
  • Manacher's algorithm uses a transformed string with sentinels to unify odd/even cases and maintains current center and right boundary.
  • Trade-offs: Manacher's is optimal but complex; expand-around-center is simpler and often fast enough.
  • Edge cases: empty string, single character, all same characters, no palindrome longer than 1.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.