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A priority queue is an abstract data type like a list or a map; just as a list can be implemented with a linked list or with an array, a priority queue can be implemented with a heap or another method such as an ordered array.
In computer science, a leftist tree or leftist heap is a priority queue implemented with a variant of a binary heap. Every node x has an s-value which is the distance to the nearest leaf in subtree rooted at x. [1] In contrast to a binary heap, a leftist tree attempts to be very unbalanced.
A bucket queue is a data structure that implements the priority queue abstract data type: it maintains a dynamic collection of elements with numerical priorities and allows quick access to the element with minimum (or maximum) priority. In the bucket queue, the priorities must be integers, and it is particularly suited to applications in which ...
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.
In computer science, a binomial heap is a data structure that acts as a priority queue.It is an example of a mergeable heap (also called meldable heap), as it supports merging two heaps in logarithmic time.
Earliest deadline first (EDF) or least time to go is a dynamic priority scheduling algorithm used in real-time operating systems to place processes in a priority queue. Whenever a scheduling event occurs (task finishes, new task released, etc.) the queue will be searched for the process closest to its deadline.
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]
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 ...