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Home Heap Performance of Heaps  
See also: Overhead  
Performance of Heaps
For both insert and remove, we perform a constant time operation to do the actual insertion and removal, but then we have to reheapify the tree. In one case we start at the root and work our way down, comparing items and then recursively reheapifying one of the subtrees. In the other case we start at a leaf and work our way up, again comparing elements at each level of the tree. As usual, there are several operations we might want to count, like comparisons and swaps. Either choice would work; the real issue is the number of levels of the tree we examine and how much work we do at each level. In both cases we keep examining levels of the tree until we restore the heap property, which means we might only visit one, or in the worst case we might have to visit them all. Let's consider the worst case. At each level, we perform only constant time operations like comparisons and swaps. So the total amount of work is proportional to the number of levels in the tree, a.k.a. the height. So we might say that these operations are linear with respect to the height of the tree, but the "problem size" we are interested in is not height, it's the number of items in the heap. As a function of n, the height of the tree is log_{2} n. This is not true for all trees, but it is true for complete trees. To see why, think of the number of nodes on each level of the tree. The first level contains 1, the second contains 2, the third contains 4, and so on. The ith level contains 2^{i} nodes, and the total number in all levels up to i is 2^{i}  1. In other words, 2^{h} = n, which means that h = log_{2} n. Thus, both insertion and removal take logarithmic time. To insert and remove n items takes time proportional to n log_{2} n.


Home Heap Performance of Heaps 