Squarepoint·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Squarepoint SWE interview with a greedy algorithm problem that looks straightforward but has a few subtle pieces worth thinking through carefully. Pretty standard coding round vibe.

Questions Asked (1)

You have n products where each product's unit price equals its current remaining stock quantity. Given m customers each buying exactly one unit, design an algorithm to maximize total revenue. Each sale reduces a product's quantity by 1 and earns that quantity as revenue before decrement.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I saw max-heap pretty quickly, always pull the largest quantity, sell it, push quantity minus one back in.

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AI HintsAI Generated

Suggested Approach

Recognize that to maximize revenue, you should always sell the product with the highest current stock (which equals its price). Use a max-heap to efficiently extract the maximum, sell one unit, decrement its quantity, and reinsert if still positive. Repeat for m customers, summing the revenues.

Pro tip: Discuss the greedy choice and its proof: selling the highest-priced item first is optimal because it reduces the price of that item for future sales, but any other choice would yield less immediate revenue and cannot lead to a better future since prices only decrease. Also mention the time complexity O((n + m) log n) and potential optimizations like early termination if all stocks are zero.

1. Understand the problem

Clarify that each product's price equals its current stock, and each sale reduces that stock by 1, earning the pre-decrement price. The goal is to maximize total revenue from m sales.

2. Identify the greedy strategy

Argue that selling the product with the highest current stock (price) first is optimal, as it yields the maximum immediate revenue and any alternative would give less now and cannot improve future revenues because prices only decrease.

3. Choose data structure

Use a max-heap (priority queue) to efficiently retrieve and update the maximum stock. Initialize the heap with all positive stock quantities.

4. Simulate the sales

For each of the m customers: extract the max, add it to total revenue, decrement it by 1, and if still positive, reinsert into the heap. If the heap is empty, stop early.

5. Analyze complexity and edge cases

Time complexity is O((n + m) log n) due to heap operations. Space is O(n). Handle cases where m exceeds total stock, or all stocks are zero initially.

Key Points to Mention

Greedy algorithm: always pick the maximum current stock.
Proof of optimality: exchange argument or induction showing that any other choice yields less revenue.
Max-heap implementation for efficient maximum extraction and updates.
Time complexity: O((n + m) log n), space O(n).
Edge cases: m larger than total stock, zero stocks, large n and m.
Potential optimization: if m is large, consider batch processing or using a sorted array if n is small.

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