Search trees | Binary trees | Amortized data structures

Scapegoat tree

In computer science, a scapegoat tree is a self-balancing binary search tree, invented by in 1989 and again by and Ronald L. Rivest in 1993. It provides worst-case lookup time (with as the number of entries) and amortized insertion and deletion time. Unlike most other self-balancing binary search trees which also provide worst case lookup time, scapegoat trees have no additional per-node memory overhead compared to a regular binary search tree: besides key and value, a node stores only two pointers to the child nodes. This makes scapegoat trees easier to implement and, due to data structure alignment, can reduce node overhead by up to one-third. Instead of the small incremental rebalancing operations used by most balanced tree algorithms, scapegoat trees rarely but expensively choose a "scapegoat" and completely rebuild the subtree rooted at the scapegoat into a complete binary tree. Thus, scapegoat trees have worst-case update performance. (Wikipedia).

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From playlist Scam City

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From playlist Data viz using Plotly for Python

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From playlist Tutorials

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From playlist Creating a Compiler

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From playlist We are like this only

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From playlist Scrum Master Training Videos | Edureka

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From playlist Pre-Algebra

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Related pages

Big O notation | Contraposition | Self-balancing binary search tree | T-tree | Data structure alignment | Red–black tree | Amortized analysis | Tree rotation | B-tree | Binary tree | Splay tree | Binary search tree | AVL tree