Heapsort - Pseudocode

Pseudocode

The following is the "simple" way to implement the algorithm in pseudocode. Arrays are zero-based and swap is used to exchange two elements of the array. Movement 'down' means from the root towards the leaves, or from lower indices to higher. Note that during the sort, the largest element is at the root of the heap at a, while at the end of the sort, the largest element is in a.

function heapSort(a, count) is input: an unordered array a of length count (first place a in max-heap order) heapify(a, count) end := count-1 //in languages with zero-based arrays the children are 2*i+1 and 2*i+2 while end > 0 do (swap the root(maximum value) of the heap with the last element of the heap) swap(a, a) (decrease the size of the heap by one so that the previous max value will stay in its proper placement) end := end - 1 (put the heap back in max-heap order) siftDown(a, 0, end) function heapify(a, count) is (start is assigned the index in a of the last parent node) start := (count - 2) / 2 while start ≥ 0 do (sift down the node at index start to the proper place such that all nodes below the start index are in heap order) siftDown(a, start, count-1) start := start - 1 (after sifting down the root all nodes/elements are in heap order) function siftDown(a, start, end) is input: end represents the limit of how far down the heap to sift. root := start while root * 2 + 1 ≤ end do (While the root has at least one child) child := root * 2 + 1 (root*2 + 1 points to the left child) swap := root (keeps track of child to swap with) (check if root is smaller than left child) if a < a swap := child (check if right child exists, and if it's bigger than what we're currently swapping with) if child+1 ≤ end and a < a swap := child + 1 (check if we need to swap at all) if swap != root swap(a, a) root := swap (repeat to continue sifting down the child now) else return

The heapify function can be thought of as building a heap from the bottom up, successively shifting downward to establish the heap property. An alternative version (shown below) that builds the heap top-down and sifts upward may be conceptually simpler to grasp. This "siftUp" version can be visualized as starting with an empty heap and successively inserting elements, whereas the "siftDown" version given above treats the entire input array as a full, "broken" heap and "repairs" it starting from the last non-trivial sub-heap (that is, the last parent node).

Also, the "siftDown" version of heapify has O(n) time complexity, while the "siftUp" version given below has O(n log n) time complexity due to its equivalence with inserting each element, one at a time, into an empty heap. This may seem counter-intuitive since, at a glance, it is apparent that the former only makes half as many calls to its logarithmic-time sifting function as the latter; i.e., they seem to differ only by a constant factor, which never has an impact on asymptotic analysis.

To grasp the intuition behind this difference in complexity, note that the number of swaps that may occur during any one siftUp call increases with the depth of the node on which the call is made. The crux is that there are many (exponentially many) more "deep" nodes than there are "shallow" nodes in a heap, so that siftUp may have its full logarithmic running-time on the approximately linear number of calls made on the nodes at or near the "bottom" of the heap. On the other hand, the number of swaps that may occur during any one siftDown call decreases as the depth of the node on which the call is made increases. Thus, when the "siftDown" heapify begins and is calling siftDown on the bottom and most numerous node-layers, each sifting call will incur, at most, a number of swaps equal to the "height" (from the bottom of the heap) of the node on which the sifting call is made. In other words, about half the calls to siftDown will have at most only one swap, then about a quarter of the calls will have at most two swaps, etc.

The heapsort algorithm itself has O(n log n) time complexity using either version of heapify.

function heapify(a,count) is (end is assigned the index of the first (left) child of the root) end := 1 while end < count (sift up the node at index end to the proper place such that all nodes above the end index are in heap order) siftUp(a, 0, end) end := end + 1 (after sifting up the last node all nodes are in heap order) function siftUp(a, start, end) is input: start represents the limit of how far up the heap to sift. end is the node to sift up. child := end while child > start parent := floor((child - 1) / 2) if a < a then (out of max-heap order) swap(a, a) child := parent (repeat to continue sifting up the parent now) else return