LinkedIn·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

LinkedIn SWE coding round, pretty standard heap problem but they threw in some follow-ups that made it more interesting than I expected.

Questions Asked (1)

Given two sorted integer arrays and an integer k, return the k pairs (one element from each array) with the smallest sums.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Got to the min-heap approach pretty quickly, seeding with the first element of nums1 paired with each element of nums2, then expanding lazily.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array sizes, k bounds, duplicates) and then propose an efficient solution using a min-heap to generate the smallest sums without computing all pairs. Explain the algorithm step-by-step, analyze its time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Demonstrate awareness of edge cases (e.g., k larger than total pairs, negative numbers) and mention how the solution scales for large arrays, showing you think beyond the basic algorithm.

1. Clarify requirements and constraints

Ask about input sizes, value ranges, whether k can exceed the total number of pairs, and if duplicates are allowed. This ensures you design the right solution.

2. Propose a heap-based approach

Explain that you can use a min-heap to store candidate pairs (i, j) with their sums, starting from (0,0). Pop the smallest sum, add it to the result, and push (i+1, j) and (i, j+1) if not already visited.

3. Walk through an example

Trace the algorithm on a small example to illustrate how pairs are generated and how duplicates are avoided using a visited set or by only pushing in one direction.

4. Analyze complexity and trade-offs

State that the time complexity is O(k log k) and space O(k). Compare with brute-force O(m*n log(m*n)) and discuss when each is appropriate.

5. Discuss optimizations and edge cases

Mention potential optimizations like early termination, handling negative numbers, and ensuring the solution works when k is larger than the total pairs.

Key Points to Mention

Min-heap (priority queue) to efficiently retrieve the smallest sums
Avoiding duplicate pairs by using a visited set or directional pushing
Time complexity O(k log k) and space complexity O(k)
Comparison with brute-force approach and when it's acceptable
Handling edge cases: k > m*n, negative numbers, empty arrays
Scalability and practical considerations for large datasets

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