AVL tree

A type of tree which uses this rebalancing technique is the AVL tree.

Interval Tree (an augmented self balancing avl tree implementation)

An AVL tree's height is strictly less than:

AVL trees and red-black trees are two examples of binary search trees that use the left rotation.

The AVL tree is another structure supporting O(log n) search, insertion, and removal.

Schemes exist for trees to automatically maintain themselves in a balanced state: AVL trees or red-black trees.

AVL trees are more rigidly balanced than red-black trees, leading to slower insertion and removal but faster retrieval.

Simple implementation-simpler than other self-balancing binary search trees, such as red-black trees or AVL trees.

AVL trees are often compared with red-black trees because they both support the same set of operations and take O(log n) time for the basic operations.

Both AVL trees and red-black trees are self-balancing binary search trees, so they are very similar mathematically.

Proteus supports associative arrays (called sets) and AVL trees, which are very useful and powerful to quickly sort and lookup values.

AVL tree, red-black tree, and splay tree, kinds of binary search tree data structures that use rotations to maintain balance.

T-trees seek to gain the performance benefits of in-memory tree structures such as AVL trees while avoiding the large storage space overhead which is common to them.

In addition, BATON tries to keep the tree in a balanced manner as the AVL tree.

This is loosely similar to AVL trees, in that the actual rotations depend on 'balances' of nodes, but the means of determining the balance differs greatly.

Tree rotations are used in a number of tree data structures such as AVL trees, red-black trees, splay trees, and treaps.

Similar to red-black trees, AVL trees are height-balanced, but in general not weight-balanced nor μ-balanced; that is, sibling nodes can have hugely differing numbers of descendants.

Access to advanced data structures (files, arrays, queues, stacks, AVL trees, sets and so on) takes place by using handles, i.e. integer numbers returned by item creation functions.

With Georgy Adelson-Velsky, he invented the AVL tree datastructure (where "AVL" stands for Adelson-Velsky Landis).

Specifically, Lehman and Carey's article describes a T-tree balanced like an AVL tree: it becomes out of balance when a node's child trees differ in height by at least two levels.

In computer science, an AVL tree (Adelson-Velskii and Landis' tree, named after the inventors) is a self-balancing binary search tree, and it was the first such data structure to be invented.

In an AVL tree, the heights of the two child subtrees of any node differ by at most one; if at any time they differ by more than one, rebalancing is done to restore this property.

The AVL tree is named after its two Soviet inventors, G. M. Adelson-Velskii and E. M. Landis, who published it in their 1962 paper "An algorithm for the organization of information".

Since AVL trees check the balance value on every insertion/deletion, it is typically stored in each node; scapegoat trees are able to calculate it only as needed, which is only when a scapegoat needs to be found.

Basic operations of an AVL tree involve carrying out the same actions as would be carried out on an unbalanced binary search tree, but modifications are followed by zero or more operations called tree rotations, which help to restore the height balance of the subtrees.