I jumped straight to the min-heap approach because it felt like the 'right' answer and I wanted to signal I knew it.
Start by clarifying the problem constraints (e.g., k and list sizes) and then present the optimal approach: use a min-heap to efficiently merge the lists. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss edge cases such as empty lists and duplicate values.
Pro tip: Mention that while a divide-and-conquer approach also achieves O(N log k), the heap-based solution is often more intuitive and easier to implement in an interview setting. Also, proactively discuss how you would handle very large k or streaming data.
Ask about the range of k, the size of each list, whether the lists are singly linked, and if there are any memory constraints. This shows you think about practical scenarios.
Explain that you will use a min-heap (priority queue) to efficiently extract the smallest node among the heads of the k lists. This yields O(N log k) time complexity.
Describe initializing the heap with the head of each non-empty list, then repeatedly pop the smallest node, append it to the result, and push its next node if it exists. Continue until the heap is empty.
State that time complexity is O(N log k) where N is the total number of nodes, and space complexity is O(k) for the heap. Compare with naive approaches like merging one by one (O(N k)).
Cover edge cases: empty input, some lists empty, duplicate values, and very large k. Mention alternative approaches like divide-and-conquer and when they might be preferable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.