The core logic is the same as the classic merge intervals problem but they split the input into two parallel arrays instead of giving you pairs.
Clarify that the input consists of two parallel arrays for starts and ends, then pair them into intervals. Sort the intervals by start time, then iterate through them, merging overlapping intervals by comparing the current interval's start with the previous merged interval's end. Return the merged intervals as two separate arrays or as a list of pairs, depending on the expected output format.
Pro tip: Always discuss edge cases like empty arrays, single interval, and intervals that are adjacent (e.g., [1,2] and [2,3]) to show thoroughness. Also, mention that sorting is O(n log n) and merging is O(n), so the overall time complexity is O(n log n) and space is O(n) for the output.
Confirm that the two arrays represent start and end points of intervals, and ask whether the output should be two separate arrays or a list of intervals. Also check if intervals are inclusive/exclusive and if they are initially sorted.
Combine the start and end arrays into a list of intervals (e.g., tuples). Sort the intervals by their start times to ensure we can merge in a single pass.
Initialize a result list with the first interval. Iterate through the sorted intervals; if the current interval's start is less than or equal to the last merged interval's end, update the last interval's end to the maximum of the two ends. Otherwise, add the current interval to the result.
If the output requires two separate arrays, extract the starts and ends from the merged intervals. Otherwise, return the list of merged intervals.
State the time complexity O(n log n) due to sorting and space complexity O(n) for the output. Discuss edge cases such as empty input, single interval, all overlapping, and non-overlapping intervals.
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