Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Stripe coding round, one problem the whole session. The problem looked manageable at first glance but the objective function has this absolute-difference term that trips you up if you're not careful about what you're actually minimizing.

Questions Asked (1)

You have exactly 3 factories, each with a list of options where each option is a cost-distance pair. Pick exactly one option per factory to minimize the total selected costs plus the sum of absolute differences between consecutive picked distances. Return the minimum total, and discuss brute-force enumeration versus whether sorting by distance allows any pruning.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Brute force is fine here since you only have 3 factories, just enumerate all triples and compute the objective.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then propose a dynamic programming solution that processes factories in order, tracking the chosen distance from the previous factory. Discuss brute-force enumeration as a baseline and analyze whether sorting by distance enables pruning, concluding that while sorting can help with early termination in some cases, it does not reduce the worst-case complexity.

Pro tip: Always state the time and space complexity of your solution and compare it to the brute-force approach. Mention that for exactly 3 factories, brute-force is O(n^3) which might be acceptable for small n, but DP is O(n^2) and generalizes to more factories.

1. Clarify the problem and constraints

Ask about the number of options per factory, the range of costs and distances, and whether the number of factories is fixed at 3 or variable. Confirm that the objective is to minimize the sum of selected costs plus the sum of absolute differences between consecutive selected distances.

2. Propose a brute-force baseline

Enumerate all possible combinations of one option per factory. For 3 factories, this is O(n^3) where n is the number of options per factory. Calculate the total for each combination and keep the minimum.

3. Develop a dynamic programming solution

Define DP state as the minimum total cost up to factory i, given the selected distance at factory i. Transition by considering all options at factory i+1 and adding the cost of the new option plus the absolute difference between distances. The answer is the minimum over all options at the last factory.

4. Analyze sorting and pruning

Discuss whether sorting options by distance allows pruning. Sorting can help in branch-and-bound for brute-force, but for DP it does not reduce the state space. However, if costs are monotonic with distance, some pruning might be possible, but generally the DP remains O(n^2).

5. Summarize and compare approaches

Conclude that DP is more efficient for larger n and generalizes to more factories, while brute-force is simpler but only feasible for small n. Mention that sorting does not change the asymptotic complexity of the optimal solution.

Key Points to Mention

Dynamic programming state definition and transition
Time and space complexity: O(n^2) for DP vs O(n^3) for brute-force
The role of sorting in pruning and its limitations
Generalization to more factories
Edge cases: single factory, large n, negative costs (if allowed)
Trade-offs between simplicity and efficiency

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