Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

One coding round at Bytedance for a software engineer role. The problem was a mashup of two classic graph problems and required finding the point with the maximum distance sum. Pretty standard algorithmic stuff but the hybrid nature kept it from being a straight copy-paste solution.

Questions Asked (1)

Given a grid, find the point that maximizes the sum of distances to all other points. The problem blends island-connectivity logic with a water-rising traversal concept.

Algorithms & Data Structures

Author's notes

The hybrid framing threw me a bit.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: the grid likely contains land cells (islands) and water cells, and we need to find a point (probably on land) that maximizes the sum of distances to all other land points. Then, discuss how to model the grid as a graph, identify connected components (islands), and compute distances efficiently, possibly using multi-source BFS or dynamic programming. Finally, analyze the time and space complexity and consider edge cases.

Pro tip: Demonstrate awareness of the trade-offs between different distance metrics (Manhattan vs. Euclidean) and how the water-rising traversal concept might imply a BFS from water to land, which can be leveraged to compute distances from all points to the target set efficiently.

1. Clarify the problem

Ask clarifying questions to understand the grid representation, what constitutes a 'point', the distance metric, and the role of water-rising traversal. Confirm whether the point must be on land and whether distances are to all other points or only to land points.

2. Model as a graph

Treat each cell as a node, with edges between adjacent cells (4-directional or 8-directional). Identify connected components (islands) using BFS/DFS. The water-rising concept suggests that water spreads from outside, so consider multi-source BFS from water cells to compute distances to land.

3. Compute distances efficiently

For each land cell, compute the sum of distances to all other land cells. Use multi-source BFS from all land cells simultaneously to get distance maps, or use dynamic programming if the grid is small. Alternatively, if the distance metric is Manhattan, use the fact that sum of Manhattan distances can be computed from row and column sums.

4. Find the maximizing point

Iterate over all candidate points (likely land cells) and compute the sum of distances using precomputed distance maps. Keep track of the maximum sum and the corresponding point. If multiple points have the same maximum, clarify tie-breaking rules.

5. Analyze complexity and edge cases

Discuss time and space complexity (e.g., O(N*M) for BFS). Consider edge cases: empty grid, no land cells, single land cell, multiple disconnected islands, and large grids. Mention optimizations like early termination or pruning if applicable.

Key Points to Mention

Graph representation of the grid and connected components (islands).
Multi-source BFS for computing distances from all points to a set of targets.
Water-rising traversal as a BFS from water to land to compute distances to water (or vice versa).
Distance metrics: Manhattan vs. Euclidean, and their implications.
Time and space complexity analysis, including optimizations for large grids.
Edge cases: empty grid, no land, single land cell, multiple islands, and tie-breaking.

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