Heap Sort: Difference between revisions
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For each element we need to sort, insert it in a [[Heap#Min_Heap_and_Max_Heap|min heap]], using the heap's <tt>[[Heap#INSERT|INSERT]]</tt> operation. The running time of an individual <tt>INSERT</tt> is O(log n), so the insertion in a loop will have a running time of O(n log n). Once all the elements that need to be sorted are placed in the min heap, extract the minimum element from the heap, also in a loop, and place it in order in the final array. The running time of the second phase is O(n log n). The total running time is O(n log n). | For each element we need to sort, insert it in a [[Heap#Min_Heap_and_Max_Heap|min heap]], using the heap's <tt>[[Heap#INSERT|INSERT]]</tt> operation. The running time of an individual <tt>INSERT</tt> is O(log n), so the insertion in a loop will have a running time of O(n log n). Once all the elements that need to be sorted are placed in the min heap, extract the minimum element from the heap, also in a loop, and place it in order in the final array. The running time of the second phase is O(n log n). The total running time is O(n log n). | ||
Revision as of 22:15, 9 October 2021
Internal
Overview
| Worst-case time | O(n lgn) |
| Average-case time | |
| Best-case time |
Algorithm
For each element we need to sort, insert it in a min heap, using the heap's INSERT operation. The running time of an individual INSERT is O(log n), so the insertion in a loop will have a running time of O(n log n). Once all the elements that need to be sorted are placed in the min heap, extract the minimum element from the heap, also in a loop, and place it in order in the final array. The running time of the second phase is O(n log n). The total running time is O(n log n).