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Bytedance·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
Jun 2026

Summary

Bytedance coding round for a software engineer role. One algorithm question, pretty standard on the surface but they clearly wanted you to know more than just the brute force approach.

Questions Asked (1)

Q1

Given a string, find the longest substring that is a palindrome. Walk through your approach and explain the time complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I went with expand-around-center which is O(n^2) and got it working, but they pushed on whether I knew Manacher's algorithm.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present a solution using center expansion, which is intuitive and O(n^2) time. Explain the approach step-by-step, analyze time and space complexity, and discuss trade-offs with other methods like Manacher's algorithm.

Pro tip: Mention Manacher's algorithm as an O(n) alternative but note that center expansion is often preferred in interviews due to simplicity and lower constant factors. This shows you know the optimal solution but can make practical trade-offs.

1. Clarify the problem

Ask about input constraints, character set, and whether the substring must be contiguous. Confirm that we need the longest palindromic substring, not subsequence.

2. Discuss brute force and its complexity

Mention that checking all substrings takes O(n^3) time, which is inefficient. This sets the stage for a better approach.

3. Present center expansion approach

Explain that a palindrome mirrors around its center. For each of the 2n-1 centers (including between characters), expand outward while characters match, tracking the longest.

4. Analyze time and space complexity

State that time complexity is O(n^2) because each expansion can take O(n) and there are O(n) centers. Space complexity is O(1) as we only store indices.

5. Discuss alternatives and trade-offs

Mention Manacher's algorithm for O(n) time but note its complexity. Also, briefly discuss dynamic programming O(n^2) time and space, and why center expansion is often preferred.

Key Points to Mention

  • Definition of a palindrome and that we need a contiguous substring.
  • Center expansion handles both odd and even length palindromes by considering 2n-1 centers.
  • Time complexity O(n^2) and space complexity O(1) for center expansion.
  • Manacher's algorithm achieves O(n) time but is more complex to implement.
  • Dynamic programming approach uses O(n^2) time and O(n^2) space, which is less efficient in space.
  • 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.