https://github.com/pfalcon/beap

Beap (bi-parental heap) algorithm reference implementation in Python
https://github.com/pfalcon/beap

algorithms algorithms-and-data-structures data-structures implicit search-algorithm

Last synced: about 2 months ago
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Beap (bi-parental heap) algorithm reference implementation in Python

• Host: GitHub
• URL: https://github.com/pfalcon/beap
• Owner: pfalcon
• License: mit
• Created: 2017-12-02T09:17:52.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-20T14:29:21.000Z (almost 7 years ago)
• Last Synced: 2024-10-10T15:10:49.685Z (3 months ago)
• Topics: algorithms, algorithms-and-data-structures, data-structures, implicit, search-algorithm
• Language: Python
• Homepage: https://en.wikipedia.org/wiki/Beap
• Size: 11.7 KB
• Stars: 13
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Beap

====

Beap aka biparental heap (https://en.wikipedia.org/wiki/Beap) is an

[implicit data structure][1] which allows efficient insertion and searching

of elements, while not requiring any more space than the data elements

themselves. Efficient searching and insertion are supported by clever

arrangement of elements in the array which backs the beap structure,

and clever algorithms utilizing this arrangement.

Beap is further elaboration of the ideas of [heap][2] data structure

(which allows for efficient insertion and finding minimum/maximum, but

not efficient searching for an arbitrary element). It was originally

[described][3] by researchers Ian Munro and Hendra Suwanda. Howerver,

it's quite hard to find an implementation of this data structure. Nor

it's easy to implement it based on the original paper, because, following

a good academic tradition, the paper is rather terse, incomplete,

inconsistent and has errata.

This project is an implementation of Beap using Python3, written by

Paul Sokolovsky and distributed under the terms of OpenSource MIT license.

It's intended to be a reference implementation, i.e. written more for

clarity and testability rather than optimized. It's accompanied by

detailed unit tests. The idea is that this implementation can serve

as the basis for more efficient implementation, if such is needed.

For more information on implicit data structures, see

https://github.com/pfalcon/awesome-implicit-data-structures .

[1]: https://en.wikipedia.org/wiki/Implicit_data_structure

[2]: https://en.wikipedia.org/wiki/Heap_(data_structure)

[3]: http://www.sciencedirect.com/science/article/pii/0022000080900379