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A heap on n elements can be merged with a heap on k elements using O(log n log k) key comparisons, or, in case of a pointer-based implementation, in O(log n log k) time. [14] An algorithm for splitting a heap on n elements into two heaps on k and n-k elements, respectively, based on a new view of heaps as an ordered collections of subheaps was ...
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Example of a binary max-heap with node keys being integers between 1 and 100. In computer science, a heap is a tree-based data structure that satisfies the heap property: In a max heap, for any given node C, if P is the parent node of C, then the key (the value) of P is greater than or equal to the key of C.
In computer science, a min-max heap is a complete binary tree data structure which combines the usefulness of both a min-heap and a max-heap, that is, it provides constant time retrieval and logarithmic time removal of both the minimum and maximum elements in it. [2]
The heapsort algorithm can be divided into two phases: heap construction, and heap extraction. The heap is an implicit data structure which takes no space beyond the array of objects to be sorted; the array is interpreted as a complete binary tree where each array element is a node and each node's parent and child links are defined by simple arithmetic on the array indexes.
A heap uses approximately 2*log(k) comparisons in each step because it handles the tree from the root down to the bottom and needs to compare both children of each node. Meanwhile, a tournament tree only needs log(k) comparisons because it starts on the bottom of the tree and works up to the root, only making a single comparison in each layer.
Heap files are lists of unordered records of variable size. Although sharing a similar name, heap files are widely different from in-memory heaps. In-memory heaps are ordered, as opposed to heap files. Simplest and most basic method insert efficient, with new records added at the end of the file, providing chronological order
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