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  2. Binary tree - Wikipedia

    en.wikipedia.org/wiki/Binary_tree

    A labeled binary tree of size 9 (the number of nodes in the tree) and height 3 (the height of a tree defined as the number of edges or links from the top-most or root node to the farthest leaf node), with a root node whose value is 1.

  3. Rotation distance - Wikipedia

    en.wikipedia.org/wiki/Rotation_distance

    Tree rotation. A binary tree is a structure consisting of a set of nodes, one of which is designated as the root node, in which each remaining node is either the left child or right child of some other node, its parent, and in which following the parent links from any node eventually leads to the root node.

  4. Tree traversal - Wikipedia

    en.wikipedia.org/wiki/Tree_traversal

    To traverse arbitrary trees (not necessarily binary trees) with depth-first search, perform the following operations at each node: If the current node is empty then return. Visit the current node for pre-order traversal. For each i from 1 to the current node's number of subtrees − 1, or from the latter to the former for reverse traversal, do:

  5. Treap - Wikipedia

    en.wikipedia.org/wiki/Treap

    To search for a given key value, apply a standard binary search algorithm in a binary search tree, ignoring the priorities. To insert a new key x into the treap, generate a random priority y for x. Binary search for x in the tree, and create a new node at the leaf position where the binary search determines a node for x should exist.

  6. Random binary tree - Wikipedia

    en.wikipedia.org/wiki/Random_binary_tree

    Binary trees may also be studied with all nodes unlabeled, or with labels that are not given in sorted order. For instance, the Cartesian tree data structure uses labeled binary trees that are not necessarily binary search trees. [4] A random binary tree is a random tree drawn from a certain probability distribution on binary trees. In many ...

  7. Weight-balanced tree - Wikipedia

    en.wikipedia.org/wiki/Weight-balanced_tree

    The size of an internal node is the sum of sizes of its two children, plus one: (size[n] = size[n.left] + size[n.right] + 1). Based on the size, one defines the weight to be weight[n] = size[n] + 1. [a] Weight has the advantage that the weight of a node is simply the sum of the weights of its left and right children. Binary tree rotations.

  8. Splay tree - Wikipedia

    en.wikipedia.org/wiki/Splay_tree

    size(r) = the number of nodes in the sub-tree rooted at node r (including r). rank(r) = log 2 (size(r)). Φ = the sum of the ranks of all the nodes in the tree. Φ will tend to be high for poorly balanced trees and low for well-balanced trees. To apply the potential method, we first calculate ΔΦ: the change in the potential caused by a splay ...

  9. Kraft–McMillan inequality - Wikipedia

    en.wikipedia.org/wiki/Kraft–McMillan_inequality

    Example for binary tree. Red nodes represent a prefix tree. The method for calculating the number of descendant leaf nodes in the full tree is shown. First, let us show that the Kraft inequality holds whenever the code for is a prefix code.