Sort by start time first, that part came to me quick.
Start by clarifying the problem: intervals are inclusive, input may be unsorted, and the output should be a list of merged intervals. Then propose sorting intervals by start time and iterating through them, merging when the current interval overlaps with the last merged interval. Analyze time and space complexity, and discuss edge cases like empty input or single interval.
Pro tip: At Optiver, emphasize robustness and efficiency: mention that sorting is O(n log n) and the merge is O(n), and proactively discuss how to handle edge cases like empty input or intervals that touch at endpoints (e.g., [1,2] and [2,3] should merge if inclusive).
Ask whether intervals are inclusive, if input is sorted, and what to return for empty input. Confirm that overlapping includes touching endpoints.
Sort the intervals based on their start values. This ensures that any overlapping intervals are adjacent, simplifying the merge process.
Initialize a result list with the first interval. For each subsequent interval, if it overlaps with the last interval in the result (i.e., its start <= last end), merge them by updating the last interval's end to the maximum of both ends. Otherwise, add it to the result.
State that time complexity is O(n log n) due to sorting, and space is O(n) for the output. Walk through a few test cases, including overlapping, non-overlapping, and edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.