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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 ...
C++ reference for std::priority_queue; Descriptions by Lee Killough; libpqueue is a generic priority queue (heap) implementation (in C) used by the Apache HTTP Server project. Survey of known priority queue structures by Stefan Xenos; UC Berkeley - Computer Science 61B - Lecture 24: Priority Queues (video) - introduction to priority queues ...
In computer science, a double-ended priority queue (DEPQ) [1] or double-ended heap [2] is a data structure similar to a priority queue or heap, but allows for efficient removal of both the maximum and minimum, according to some ordering on the keys (items) stored in the structure. Every element in a DEPQ has a priority or value.
In computer science, a strict Fibonacci heap is a priority queue data structure with low worst case time bounds. It matches the amortized time bounds of the Fibonacci heap in the worst case. To achieve these time bounds, strict Fibonacci heaps maintain several invariants by performing restoring transformations after every operation.
Queue (example Priority queue) Double-ended queue; Graph (example Tree, Heap) ... These are data structures used for space partitioning or binary space partitioning.
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.
Double-ended queues can also be implemented as a purely functional data structure. [3]: 115 Two versions of the implementation exist. The first one, called 'real-time deque, is presented below. It allows the queue to be persistent with operations in O(1) worst-case time, but requires lazy lists with memoization. The second one, with no lazy ...
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.