← Interactive Interview Insights
My first instinct was simulation, just track the last day each product type was produced and advance a day counter accordingly.
Model the problem as a task scheduling problem with cooldown constraints. Use a greedy approach with a max-heap to always produce the most frequent remaining product, and a queue to track products in cooldown. Simulate day by day, incrementing the day count until all products are produced.
Pro tip: Clarify whether the cooling period applies to consecutive productions of the same product or any same product within the period. Also, discuss edge cases like empty list or cooling period 0, and mention that the greedy approach is optimal for this problem.
Restate the problem: given a list of product types and a cooling period n, find the minimum days to produce all products without producing the same type within n days. Clarify input/output and edge cases.
Use a frequency map to count occurrences of each product. Use a max-heap to efficiently retrieve the most frequent available product, and a queue to manage products in cooldown with their remaining cooldown days.
Iterate day by day. Each day, if the heap is not empty, pop the most frequent product, decrement its count, and if still >0, add it to the cooldown queue with cooldown n. Also, check the queue for any product whose cooldown expires today and push it back to the heap.
If the heap is empty but the queue is not, it means we must idle. Increment the day count and continue. Stop when both heap and queue are empty. Return the total days.
Discuss time complexity O(total tasks * log k) where k is number of unique products, and space O(k). Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.