Start by clarifying the problem constraints (e.g., list size, data types, whether the list can be empty) and then propose a simple linear scan solution. Discuss time and space complexity, and consider edge cases and potential optimizations or alternative approaches.
Pro tip: Mention that while a linear scan is optimal for unsorted data, if the list is sorted or nearly sorted, binary search or other techniques could be more efficient. Also, emphasize the importance of handling edge cases like empty lists or single-element lists.
Ask about input size, data types, whether the list can be empty, and if there are any constraints on time or space complexity.
Describe a linear scan approach: initialize min to the first element, iterate through the list, and update min when a smaller element is found.
State that the time complexity is O(n) and space complexity is O(1), which is optimal for unsorted data.
Discuss how to handle an empty list (e.g., return null or throw an exception) and a single-element list.
Mention that if the list is sorted, the minimum is the first element (O(1)), or if the list is very large and distributed, parallel reduction could be used.
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