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There is no support for the replace, sift-up/sift-down, or decrease/increase-key operations. Python has a heapq module that implements a priority queue using a binary heap. The library exposes a heapreplace function to support k-way merging. PHP has both max-heap (SplMaxHeap) and min-heap (SplMinHeap) as of version 5.3 in the Standard PHP Library.
The decrease key operation replaces the value of a node with a given value with a lower value, and the increase key operation does the same but with a higher value. This involves finding the node with the given value, changing the value, and then down-heapifying or up-heapifying to restore the heap property. Decrease key can be done as follows:
STL also has utility functions for manipulating another random-access container as a binary max-heap. The Boost libraries also have an implementation in the library heap. Python's heapq module implements a binary min-heap on top of a list. Java's library contains a PriorityQueue class, which implements a min-priority-queue as a binary heap.
Python's standard library includes heapq.nsmallest and heapq.nlargest functions for returning the smallest or largest elements from a collection, in sorted order. The implementation maintains a binary heap , limited to holding k {\displaystyle k} elements, and initialized to the first k {\displaystyle k} elements in the collection.
A Fibonacci heap is a collection of trees satisfying the minimum-heap property, that is, the key of a child is always greater than or equal to the key of the parent. This implies that the minimum key is always at the root of one of the trees. Compared with binomial heaps, the structure of a Fibonacci heap is more flexible.
While tolerating arbitrary objects, even heterogeneity within a single hash table instance, the specification of this key :test function is confined to distinguishable entities: the Common Lisp standard only mandates the support of eq, eql, equal, and equalp, yet designating additional or custom operations as permissive for concrete ...
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.
For priority queues and DEPQs, however, dequeuing and enqueuing often take O(log n) time (for example if implemented as a binary heap), while O(1) performance of "peek" (here generally called "find-min" or "find-max") is a key desired characteristic of priority queues, and thus peek is almost invariably implemented separately.