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index | /rank-balanced-trees/ | Rank-Balanced Trees | Explaining the rank-balanced trees. The web version of my bachelor thesis. |
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Data structures have become a regular part of the essential toolbox for problem-solving. In many cases, they also help to improve the existing algorithms’ performance, e.g., using a priority queue in Dijkstra’s algorithm for the shortest path. This thesis will mainly discuss the implementation of a set.
Currently, the most commonly used implementations of sets use hash tables, but we will talk about another common alternative, implementation via self-balancing search trees. Compared to a hash table, they provide consistent time complexity, but at the cost of a requirement for ordering on the elements. The most implemented self-balancing binary tree is a red-black tree, as described by Guibas and Sedgewick1. Among other alternatives, we can find (non-binary) B-tree2 and AVL tree3.
We will focus on the Weak AVL (WAVL) tree4 that is a relaxed variant of the AVL tree, but still provides better balancing than a red-black tree.
We will start by describing the AVL tree, then we will introduce the idea of a rank-balanced tree. Given this insight we will be able to explore various implementations of aforementioned trees using the rank-balanced tree. At the end we will focus on the Weak AVL tree
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ADELSON-VELSKIJ, Georgij; LANDIS, Evgenij. An algorithm for the organization of information. Doklady Akad. Nauk SSSR. 1962, vol. 146, pp. 263–266. ↩︎