Sort by start time first, that's the whole key.
Start by clarifying the problem and edge cases, then propose sorting the intervals by start time. After sorting, iterate through the intervals and merge overlapping ones by comparing the current interval's start with the previous merged interval's end. Return the merged list.
Pro tip: Mention that sorting is key to achieving O(n log n) time, and that you can optimize space by merging in-place if the input can be modified. Also, discuss how you would handle edge cases like empty input or single interval.
Ask clarifying questions: Are intervals sorted? Can they be modified? What is the expected output format? Confirm edge cases like empty input or single interval.
Sort the intervals by their start times. This ensures that any overlapping intervals are adjacent, simplifying the merging process.
Initialize a result list with the first interval. Iterate through the sorted intervals, and if the current interval overlaps with the last interval in the result, merge them by updating the end time. Otherwise, add the current interval to the result.
State the time complexity: O(n log n) due to sorting, and O(n) for the merge pass. Space complexity is O(n) for the output, or O(1) extra if merging in-place.
Walk through a few test cases, including overlapping intervals, non-overlapping intervals, and edge cases like empty input, to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.