In computer science, a heap is a specialized tree-based data structure that satisfies the heap property: if B is a child node of A, then key(A) ≥ key(B). This implies that an element with the greatest key is always in the root node, and so such a heap is sometimes called a max heap. (Alternatively, if the comparison is reversed, the smallest element is always in the root node, which results in a min heap.) This is why heaps are used to implement priority queues. The efficiency of heap operations is crucial in several graph algorithms.
The operations commonly performed with a heap are
Heaps are used in the sorting algorithm heapsort.
The following complexities are worst-case for binary and binomial heaps and amortized complexity for Fibonacci heap. O(f) gives asymptotic upper bound and Θ(f) is asymptotically tight bound (see Big O notation). Function names assume a min-heap.
|findMin||Θ(1)||O(lg n) or Θ(1)||Θ(1)|
|deleteMin||Θ(lg n)||Θ(lg n)||O(lg n)|
|insert||Θ(lg n)||O(lg n)||Θ(1)|
|decreaseKey||Θ(lg n)||Θ(lg n)||Θ(1)|
Interestingly, binary heaps may be represented using an array alone. The first (or last) element will contain the root. The next two elements of the array contain its children. The next four contain the four children of the two child nodes, etc. Thus the children of the node at position
n would be at positions
2n+1 in a one-based array, or
2n+2 in a zero-based array. Balancing a heap is done by swapping elements which are out of order. As we can build a heap from an array without requiring extra memory (for the nodes, for example), heapsort can be used to sort an array in-place.
One more advantage of heaps over trees in some applications is that construction of heaps can be done in linear time using Tarjan's algorithm.