I went straight to sorting and probably over-explained it.
Start by clarifying the problem requirements and edge cases, then propose a solution that groups transactions by payment method, sorts each group by date descending, and iterates through methods in the specified priority order to allocate refunds. Finally, analyze the time and space complexity and discuss potential optimizations or trade-offs.
Pro tip: Demonstrate awareness of real-world constraints: mention that refunds might need to be idempotent and that the algorithm should handle partial refunds if the refund amount is less than the payment amount, even though the problem states 'refund one payment in full'.
Ask questions to confirm assumptions: Can a payment be partially refunded? What if the refund amount exceeds total payments? Should we consider only completed transactions? How to handle ties in dates?
Group transactions by method using a hash map, sort each group by date descending, then iterate through methods in priority order (CREDIT, CREDIT_CARD, PAYPAL) and within each method refund full payments until the refund amount is exhausted.
Write pseudocode or actual code, ensuring to handle cases where the refund amount is less than a payment amount (partial refund) and when the refund amount exceeds total available.
Explain that sorting dominates time complexity: O(n log n) overall, where n is number of transactions. Space complexity O(n) for storing grouped transactions. Discuss alternative approaches like using a priority queue per method.
Walk through a small example to verify correctness, including edge cases like refund amount exactly matching a payment or exceeding total.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.