Pinterest·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Pinterest coding round with a graph/finance problem that looked straightforward but had a lot of depth once they started asking follow-ups about scale and overflow. Solid problem, decent experience overall.

Questions Asked (1)

You're given a list of debts between people as (debtor, creditor, amount) triples. Return the minimum number of transfer transactions needed to fully settle all balances. Any valid optimal solution is fine.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I jumped straight to simulating the original edges and got tangled up.

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AI HintsAI Generated

Suggested Approach

First, compute each person's net balance by summing credits and debits. Then, use a greedy or backtracking approach to settle the balances with the minimum number of transactions, leveraging the fact that the total sum of balances is zero. Focus on explaining the algorithm and its complexity, and discuss potential optimizations.

Pro tip: Mention that the problem is NP-hard in general (minimum transactions is equivalent to partitioning into zero-sum subsets), so for large inputs a greedy approach is often used, but for small n, backtracking with pruning can find the optimal. This shows awareness of practical constraints.

1. Compute Net Balances

Iterate through the list of debts and calculate the net balance for each person (credits minus debits). Only people with non-zero balances need to be considered.

2. Choose an Algorithm

Decide between a greedy approach (settle largest creditor with largest debtor) and an optimal backtracking approach. Explain the trade-offs: greedy is O(n log n) but may not be optimal; backtracking is exponential but finds the minimum.

3. Implement Settlement

For greedy: repeatedly match the largest positive balance with the largest negative balance, transferring the minimum of the two amounts. For optimal: use DFS/backtracking to try all possible settlements, pruning when the current transaction count exceeds the best found.

4. Analyze Complexity and Edge Cases

Discuss time and space complexity. Handle edge cases: no debts, already settled, large amounts, and floating-point precision if amounts are not integers.

5. Test and Validate

Walk through a small example to verify correctness. Compare greedy vs optimal on a case where greedy fails to show understanding.

Key Points to Mention

Net balance calculation reduces the problem to settling balances among a subset of people.
The problem is equivalent to finding the minimum number of edges in a graph that balances all nodes, which is NP-hard (related to partition problem).
Greedy approach: sort balances, use two pointers to match largest creditor and debtor; time complexity O(n log n).
Optimal approach: backtracking with pruning, trying all possible transfers; can be optimized by grouping equal balances.
Edge cases: zero balances, single person, all balances zero, large number of people.
Trade-off between optimality and efficiency; in practice, greedy is often acceptable for large inputs.

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