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Heapsort is a comparison-based sorting algorithm, and is part of the selection sort family. Although somewhat slower in practice on most machines than a well implemented quicksort, it has the advantage of a more favorable worst-case Θ(n log n) runtime. Heapsort is an in-place algorithm, but is not a stable sort.



Heapsort begins by building a heap out of the data set, and then removing the largest item and placing it at the end of the partially sorted array. After removing the largest item, it reconstructs the heap, removes the largest remaining item, and places it in the next open position from the end of the partially sorted array. This is repeated until there are no items left in the heap and the sorted array is full. Elementary implementations require two arrays - one to hold the heap and the other to hold the sorted elements.

Heapsort inserts the input list elements into a binary heap data structure. The largest value (in a max-heap) or the smallest value (in a min-heap) are extracted until none remain, the values having been extracted in sorted order. The heap's invariant is preserved after each extraction, so the only cost is that of extraction.

During extraction, the only space required is that needed to store the heap. To achieve constant space overhead, the heap is stored in the part of the input array not yet sorted. (The storage of heaps as arrays is diagrammed at Binary heap#Heap implementation.)

Heapsort uses two heap operations: insertion and root deletion. Each extraction places an element in the last empty location of the array. The remaining prefix of the array stores the unsorted elements.


  • The most important variation to the simple variant is an improvement by R. W. Floyd that, in practice, gives about a 25% speed improvement by using only one comparison in each siftup run, which must be followed by a siftdown for the original child. Moreover, it is more elegant to formulate. Heapsort's natural way of indexing works on indices from 1 up to the number of items. Therefore the start address of the data should be shifted such that this logic can be implemented avoiding unnecessary +/- 1 offsets in the coded algorithm.
  • Ternary heapsort[2] uses a ternary heap instead of a binary heap; that is, each element in the heap has three children. It is more complicated to program, but does a constant number of times fewer swap and comparison operations. This is because each step in the shift operation of a ternary heap requires three comparisons and one swap, whereas in a binary heap two comparisons and one swap are required. The ternary heap does two steps in less time than the binary heap requires for three steps, which multiplies the index by a factor of 9 instead of the factor 8 of three binary steps. Ternary heapsort is about 12% faster than the simple variant of binary heapsort.[citation needed]
  • The smoothsort algorithm[3][4] is a variation of heapsort developed by Edsger Dijkstra in 1981. Like heapsort, smoothsort's upper bound is O(n log n). The advantage of smoothsort is that it comes closer to O(n) time if the input is already sorted to some degree, whereas heapsort averages O(n log n) regardless of the initial sorted state. Due to its complexity, smoothsort is rarely used.
  • Levcopoulos and Petersson[5] describe a variation of heapsort based on a Cartesian tree that does not add an element to the heap until smaller values on both sides of it have already been included in the sorted output. As they show, this modification can allow the algorithm to sort more quickly than O(n log n) for inputs that are already nearly sorted.
  • Ingo Wegener[6] describes a bottom up version of heapsort that replaces siftdown with an alternative that reduces the worst case from 2n log(n) to 1.5n log(n) and is claimed to perform better than some versions of quicksort.

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