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Using min heap priority queue in Prim's algorithm to find the minimum spanning tree of a connected and undirected graph, one can achieve a good running time. This min heap priority queue uses the min heap data structure which supports operations such as insert, minimum, extract-min, decrease-key. [23]
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.
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.
A min-priority queue is an abstract data type that provides 3 basic operations: add_with_priority(), decrease_priority() and extract_min(). As mentioned earlier, using such a data structure can lead to faster computing times than using a basic queue. Notably, Fibonacci heap [19] or Brodal queue offer optimal implementations for those 3 ...
In computer science, a Fibonacci heap is a data structure for priority queue operations, consisting of a collection of heap-ordered trees.It has a better amortized running time than many other priority queue data structures including the binary heap and binomial heap.
create-queue(q): create an empty kinetic priority queue q; find-max(q, t) (or find-min): - return the max (or min for a min-queue) value stored in the queue q at the current virtual time t. insert(X, f X, t): - insert a key X into the kinetic queue at the current virtual time t, whose value changes as a continuous function f X (t) of time t.
Double-ended priority queues can be built from balanced binary search trees (where the minimum and maximum elements are the leftmost and rightmost leaves, respectively), or using specialized data structures like min-max heap and pairing heap. Generic methods of arriving at double-ended priority queues from normal priority queues are: [5]
Chen et al. [11] examined priority queues specifically for use with Dijkstra's algorithm and concluded that in normal cases using a d-ary heap without decrease-key (instead duplicating nodes on the heap and ignoring redundant instances) resulted in better performance, despite the inferior theoretical performance guarantees.