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Clarify the problem requirements (e.g., input size, duplicates, order preservation) and then propose an efficient solution using a min-heap of size k to track the top k largest elements. Analyze time and space complexity, and discuss potential optimizations or alternative approaches.
Pro tip: Mention that for very large datasets, a streaming approach with a min-heap is more memory-efficient than sorting the entire list, and highlight that the heap approach can handle duplicates naturally.
Ask about input size, whether duplicates count separately, if the output order matters, and if the input can be modified.
Explain using a min-heap of size k: iterate through the list, push each element, and if the heap size exceeds k, pop the smallest. At the end, the heap contains the top k largest.
State that time complexity is O(n log k) and space complexity is O(k), which is efficient for large n and small k.
Mention sorting (O(n log n)) or quickselect (average O(n)) and explain when each might be preferable, considering constraints like memory or need for order.
Cover cases like k >= n, k <= 0, empty list, and duplicates, ensuring the solution handles them gracefully.
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