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Parallel algorithms for constructing red–black trees from sorted lists of items can run in constant time or ( ) time, depending on the computer model, if the number of processors available is asymptotically proportional to the number of items where . Fast search, insertion, and deletion parallel algorithms are also known.
Self-balancing binary trees solve this problem by performing transformations on the tree (such as tree rotations) at key insertion times, in order to keep the height proportional to log 2 (n). Although a certain overhead is involved, it is not bigger than the always necessary lookup cost and may be justified by ensuring fast execution of all ...
All of the red-black tree algorithms that have been proposed are characterized by a worst-case search time bounded by a small constant multiple of log N in a tree of N keys, and the behavior observed in practice is typically that same multiple faster than the worst-case bound, close to the optimal log N nodes examined that would be observed in a perfectly balanced tree.
In 2016, Blelloch et al. formally proposed the join-based algorithms, and formalized the join algorithm for four different balancing schemes: AVL trees, red–black trees, weight-balanced trees and treaps. In the same work they proved that Adams' algorithms on union, intersection and difference are work-optimal on all the four balancing schemes.
Various height-balanced binary search trees were introduced to confine the tree height, such as AVL trees, Treaps, and red–black trees. [5] The AVL tree was invented by Georgy Adelson-Velsky and Evgenii Landis in 1962 for the efficient organization of information. [6] [7] It was the first self-balancing binary search tree to be invented. [8]
While an AA tree makes more rotations than a red–black tree, the simpler algorithms tend to be faster, and all of this balances out to result in similar performance. A red–black tree is more consistent in its performance than an AA tree, but an AA tree tends to be flatter, which results in slightly faster search times. [2]
Both AVL trees and red–black (RB) trees are self-balancing binary search trees and they are related mathematically. Indeed, every AVL tree can be colored red–black, [14] but there are RB trees which are not AVL balanced. For maintaining the AVL (or RB) tree's invariants, rotations play an important role.
One property of a 2–3–4 tree is that all external nodes are at the same depth. 2–3–4 trees are closely related to red–black trees by interpreting red links (that is, links to red children) as internal links of 3-nodes and 4-nodes, although this correspondence is not one-to-one. [2]
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