I knew sorting was the move but fumbled explaining why adjacent elements are the only pairs you need to check after sorting.
Start by clarifying the problem constraints (e.g., array size, duplicates, data types) and then propose an efficient solution: sort the array and compute the minimum difference between adjacent elements. Discuss the time and space complexity, and consider edge cases like arrays with fewer than two elements.
Pro tip: Mention that sorting is often the optimal approach for this problem because it brings potentially close elements together, and explicitly compare it to the brute-force O(n^2) method to highlight the trade-off. Also, note that if the array is already sorted or if we need to avoid modifying the input, we can copy it first.
Ask about array size, whether elements can be negative, if duplicates are allowed, and if the array can be modified. This shows attention to detail and helps choose the right approach.
Mention the naive O(n^2) solution of comparing all pairs, then propose the sorting-based O(n log n) approach as more efficient. Explain why sorting helps: adjacent elements in sorted order have the smallest possible differences.
Describe the steps: sort the array, initialize min_diff to infinity, iterate from the second element, compute difference with previous, update min_diff. Return min_diff.
State time complexity O(n log n) due to sorting, space O(1) if in-place sort or O(n) if copy. Handle edge cases: array length < 2 (return error or infinity), all elements equal (difference 0).
Mention that if the array is already sorted, we can do it in O(n). Also, if the range of numbers is small, a counting sort or bucket approach could be O(n), but sorting is generally optimal.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.