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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 ...
Priority queue (such as a heap) Double-ended queue (deque) Double-ended priority queue (DEPQ) Single-ended types, such as stack, generally only admit a single peek, at the end that is modified. Double-ended types, such as deques, admit two peeks, one at each end. Names for peek vary. "Peek" or "top" are common for stacks, while for queues ...
Queues are common in computer programs, where they are implemented as data structures coupled with access routines, as an abstract data structure or in object-oriented languages as classes. A queue has two ends, the top, which is the only position at which the push operation may occur, and the bottom, which is the only position at which the pop ...
This makes the min-max heap a very useful data structure to implement a double-ended priority queue. Like binary min-heaps and max-heaps, min-max heaps support logarithmic insertion and deletion and can be built in linear time. [3] Min-max heaps are often represented implicitly in an array; [4] hence it's referred to as an implicit data structure.
Binary heaps are a common way of implementing priority queues. [1]: 162–163 The binary heap was introduced by J. W. J. Williams in 1964 as a data structure for implementing heapsort. [2] A binary heap is defined as a binary tree with two additional constraints: [3]
The d-ary heap consists of an array of n items, each of which has a priority associated with it. These items may be viewed as the nodes in a complete d-ary tree, listed in breadth first traversal order: the item at position 0 of the array (using zero-based numbering) forms the root of the tree, the items at positions 1 through d are its children, the next d 2 items are its grandchildren, etc.
In computer science, a priority search tree is a tree data structure for storing points in two dimensions. It was originally introduced by Edward M. McCreight. [1] It is effectively an extension of the priority queue with the purpose of improving the search time from O(n) to O(s + log n) time, where n is the number of points in the tree and s is the number of points returned by the search.
The time complexity of Prim's algorithm depends on the data structures used for the graph and for ordering the edges by weight, which can be done using a priority queue. The following table shows the typical choices: