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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Bytedance software engineer interview with a classic binary search problem that looks straightforward but has a few ways to approach it depending on how much you care about optimal complexity.

Questions Asked (1)

Q1

Given two sorted arrays and an integer k, find the k-th smallest element across both arrays combined.

Algorithms & Data Structures
Author's notes

My first instinct was the two-pointer merge approach, which works but runs in O(k) and I could tell they wanted something better.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array sizes, duplicates, k validity) and discussing naive approaches like merging or using a heap. Then present an optimal binary search solution that partitions the arrays to find the k-th element in O(log(min(m,n))) time, explaining the logic and edge cases.

Pro tip: Emphasize the importance of handling edge cases such as empty arrays, k out of bounds, and duplicates; also mention that this approach can be extended to find the median of two sorted arrays, a common variant.

1. Clarify constraints and edge cases

Ask about array sizes, whether duplicates are allowed, and if k is guaranteed to be valid. Discuss how to handle empty arrays or k=0.

2. Discuss naive approaches

Mention merging the arrays (O(m+n)) or using a min-heap of size k (O(k log k)) and their trade-offs in time and space.

3. Present optimal binary search approach

Explain partitioning the smaller array and binary searching for the correct split such that the left half contains exactly k elements and all left elements are ≤ all right elements.

4. Walk through an example

Trace the algorithm on a small example to demonstrate correctness and how the binary search narrows down the partition.

5. Analyze complexity and edge cases

State time complexity O(log(min(m,n))) and space O(1). Discuss handling duplicates and ensuring indices are within bounds.

Key Points to Mention

  • Binary search on the smaller array to achieve O(log(min(m,n))) time.
  • Partitioning logic: ensure left half has k elements and max(left) ≤ min(right).
  • Handling edge cases: empty arrays, k=0, k=m+n, duplicates.
  • Comparison with alternative approaches (merge, heap) and why binary search is optimal.
  • Extension to finding the median of two sorted arrays.
  • Use of infinity sentinels to simplify boundary conditions.

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