Kneron·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Interviewed for a software engineer role at Kneron and got a fairly involved algorithmic problem involving a simulation on an array. Not a typical sorting or greedy question, took me a minute to figure out what data structure actually makes sense here.

Questions Asked (1)

You have an array of positive integers representing product weights. Repeatedly pick the lightest element (breaking ties by smallest current index), add its weight to a running total, and remove it along with up to its two immediate neighbors in the current array. Return the final total once all elements are gone.

Algorithms & Data Structures

Author's notes

Took me longer than I'd like to admit to nail down the neighbor logic.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient algorithm using a priority queue (min-heap) to repeatedly select the lightest element, while managing removals and neighbor updates with a doubly linked list. Analyze time and space complexity, and discuss potential optimizations or alternative approaches.

Pro tip: Demonstrate awareness of tie-breaking rules and dynamic index changes by explicitly stating how you maintain the current order (e.g., using a linked list) and how you handle stale entries in the heap. This shows attention to detail and robustness.

1. Understand the problem

Restate the problem in your own words, confirm the tie-breaking rule (lightest weight, then smallest current index), and ask clarifying questions about constraints (e.g., array size, weight range).

2. Design the algorithm

Propose using a min-heap to efficiently select the lightest element, and a doubly linked list to represent the current array for O(1) neighbor removal and index updates. Explain how to handle stale heap entries.

3. Walk through an example

Trace the algorithm on a small example to illustrate the process, including tie-breaking and neighbor removal, ensuring the logic is clear.

4. Analyze complexity

State the time complexity (O(n log n) due to heap operations) and space complexity (O(n) for heap and linked list), and discuss if any optimizations are possible.

5. Handle edge cases

Mention edge cases such as single element, all equal weights, and large inputs, and explain how the algorithm handles them.

Key Points to Mention

Use of a min-heap (priority queue) to efficiently find the lightest element.
Maintaining a doubly linked list to represent the current array and enable O(1) removal of neighbors.
Handling stale entries in the heap by checking if the element is still active before processing.
Tie-breaking by smallest current index: ensure the heap comparator uses weight and then index.
Time complexity: O(n log n) due to heap operations; space complexity: O(n).
Edge cases: single element, all elements equal, and large input sizes.

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