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  2. Self-balancing binary search tree - Wikipedia

    en.wikipedia.org/wiki/Self-balancing_binary...

    For height-balanced binary trees, the height is defined to be logarithmic (⁡) in the number of items. This is the case for many binary search trees, such as AVL trees and red–black trees . Splay trees and treaps are self-balancing but not height-balanced, as their height is not guaranteed to be logarithmic in the number of items.

  3. AVL tree - Wikipedia

    en.wikipedia.org/wiki/AVL_tree

    In a binary tree the balance factor of a node X is defined to be the height difference ():= (()) (()) [6]: 459 of its two child sub-trees rooted by node X. A node X with () < is called "left-heavy", one with () > is called "right-heavy", and one with () = is sometimes simply called "balanced".

  4. Binary search tree - Wikipedia

    en.wikipedia.org/wiki/Binary_search_tree

    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]

  5. Binary tree - Wikipedia

    en.wikipedia.org/wiki/Binary_tree

    A balanced binary tree is a binary tree structure in which the left and right subtrees of every node differ in height (the number of edges from the top-most node to the farthest node in a subtree) by no more than 1 (or the skew is no greater than 1). [22]

  6. Red–black tree - Wikipedia

    en.wikipedia.org/wiki/Red–black_tree

    The worst-case height of AVL is 0.720 times the worst-case height of red-black trees, so AVL trees are more rigidly balanced. The performance measurements of Ben Pfaff with realistic test cases in 79 runs find AVL to RB ratios between 0.677 and 1.077, median at 0.947, and geometric mean 0.910. [ 22 ]

  7. B-tree - Wikipedia

    en.wikipedia.org/wiki/B-tree

    A B-tree of depth n+1 can hold about U times as many items as a B-tree of depth n, but the cost of search, insert, and delete operations grows with the depth of the tree. As with any balanced tree, the cost grows much more slowly than the number of elements.

  8. Tree traversal - Wikipedia

    en.wikipedia.org/wiki/Tree_traversal

    The worst-case complexity is () with as the height of the tree. All the above implementations require stack space proportional to the height of the tree which is a call stack for the recursive and a parent (ancestor) stack for the iterative ones. In a poorly balanced tree, this can be considerable.

  9. BATON Overlay - Wikipedia

    en.wikipedia.org/wiki/BATON_Overlay

    Height-Balanced. BATON is considered balanced if and only if the height of its two sub-trees at any node in the tree differs by at most one. If any node detects that the height-balanced constraint is violated, a restructuring process is initiated to ensure that the tree remains balanced.