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First, sort the intervals by their starting point. If two intervals have the same start, sort them by their ending point in descending order. This helps in ensuring that when you iterate, you can simply keep a check on the maximum end encountered and compare each interval to determine if it is covered.
Time Complexity: O(n log n) due to sorting.
Space Complexity: O(1) since sorting is done in place.
1var removeCoveredIntervals = function(intervals) {
2 intervals.sort((a, b) => a[0] - b[0] || b[1] - a[1]);
3 let remaining = 0, prevEnd = 0;
4 for (let [, end] of intervals) {
5 if (end > prevEnd) {
6 remaining++;
7 prevEnd = end;
8 }
9 }
10 return remaining;
11};In JavaScript, the sort() method handles the ordering with comparison-based logic directly in line with the previous language strategies. As it traverses the intervals, it verifies coverage based on the historical max end position.
Conceptually, treat each pair of intervals as a directed edge if one interval is covering another. Such implicit graph construction helps identify covered intervals. You process over this graph to recognize unique intervals not covered by others. This approach may be non-trivial compared to the sorting technique, but it provides a different perspective.
Time Complexity: O(n^2) for comparing each interval with every other one.
Space Complexity: O(n) for the boolean array tracking covered intervals.
The Python function evaluates potential graph connections implying coverage. 'Covered' boolean list identifies intervals that are enveloped (becoming non-essential), hence they are decrementally ignored in the output result.