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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Bytedance SWE coding round, pretty much one meaty algorithm question that branched into a few follow-ups. Felt manageable but the follow-ups on indices and the divide-and-conquer variant caught me a bit flat-footed.

Questions Asked (2)

Q1

Given an integer array, find the contiguous subarray with the largest sum and return that sum. Expected O(n) solution.

Algorithms & Data Structures
Author's notes

Knew Kadane's going in so the core answer came out clean.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then explain the optimal O(n) Kadane's algorithm. Walk through the algorithm step-by-step with a small example, and finally discuss time/space complexity and potential follow-ups.

Pro tip: Mention that Kadane's algorithm is a classic dynamic programming approach and that you can handle all-negative arrays by initializing current and max sums to the first element. This shows attention to edge cases and deep understanding.

1. Clarify and Confirm

Ask clarifying questions: Is the array non-empty? Can it contain all negative numbers? What should be returned if the array is empty? Confirm the expected output is the sum, not the subarray itself.

2. Explain the Approach

Describe Kadane's algorithm: iterate through the array, maintaining the maximum sum ending at the current position and the overall maximum sum. At each step, decide whether to extend the previous subarray or start a new one.

3. Walk Through an Example

Use a small array like [-2,1,-3,4,-1,2,1,-5,4] to demonstrate how the algorithm works, showing the updates to current and max sums.

4. Analyze Complexity

State that the algorithm runs in O(n) time and O(1) space, which is optimal for this problem.

5. Discuss Edge Cases and Follow-ups

Mention handling of all-negative arrays, empty input, and potential follow-ups like returning the subarray indices or handling circular arrays.

Key Points to Mention

  • Kadane's algorithm as a dynamic programming solution
  • Time complexity O(n) and space complexity O(1)
  • Handling all-negative arrays by initializing to the first element
  • The recurrence relation: current_max = max(nums[i], current_max + nums[i])
  • Edge cases: empty array, single element, all negatives
  • Potential follow-up: return the subarray itself or its indices

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

Q2

How would you solve the maximum subarray problem using a divide-and-conquer approach, and what are the trade-offs compared to the linear scan approach?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Knew this existed but couldn't reconstruct it cleanly under pressure.

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AI HintsAI Generated

Suggested Approach

Start by clearly explaining the divide-and-conquer algorithm: recursively find the maximum subarray in the left and right halves, and also compute the maximum crossing subarray. Then compare its O(n log n) time and O(log n) space complexity with the linear scan's O(n) time and O(1) space, highlighting when each is preferable.

Pro tip: Mention that while divide-and-conquer is asymptotically slower, it's a great example of recursive problem-solving and can be parallelized; however, for most practical cases, Kadane's algorithm is the go-to due to its simplicity and efficiency.

1. Explain the divide-and-conquer algorithm

Describe how to split the array into two halves, recursively find the maximum subarray in each half, and then find the maximum subarray that crosses the midpoint. The overall maximum is the maximum of these three.

2. Detail the crossing subarray computation

Explain that the crossing subarray is found by scanning left from the midpoint to find the maximum suffix sum, and scanning right to find the maximum prefix sum, then adding them.

3. Analyze time and space complexity

State that the recurrence T(n) = 2T(n/2) + O(n) yields O(n log n) time, and the recursion depth gives O(log n) space (or O(n) if counting the input array).

4. Describe the linear scan (Kadane's algorithm)

Briefly explain Kadane's algorithm: iterate through the array, maintaining the maximum subarray ending at the current position and the overall maximum. It runs in O(n) time and O(1) space.

5. Compare trade-offs

Discuss that divide-and-conquer is slower but demonstrates recursive thinking and can be parallelized; linear scan is faster, simpler, and more space-efficient, making it preferable for most practical applications.

Key Points to Mention

  • Divide-and-conquer recurrence relation and its O(n log n) time complexity
  • How to compute the maximum crossing subarray in O(n) time
  • Kadane's algorithm as the linear scan approach with O(n) time and O(1) space
  • Trade-offs: simplicity, space efficiency, and practical performance favor linear scan
  • Divide-and-conquer can be parallelized and is a good demonstration of recursive problem-solving
  • Edge cases: all negative numbers, empty array, and how each algorithm handles them

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