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  2. Nested set model - Wikipedia

    en.wikipedia.org/wiki/Nested_set_model

    The nested set model is a technique for representing nested set collections (also known as trees or hierarchies) in relational databases.. It is based on Nested Intervals, that "are immune to hierarchy reorganization problem, and allow answering ancestor path hierarchical queries algorithmically — without accessing the stored hierarchy relation".

  3. Stochastic programming - Wikipedia

    en.wikipedia.org/wiki/Stochastic_programming

    For example, one can construct a particular scenario tree defining time evolution of the process. If at every stage the random return of each asset is allowed to have two continuations, independent of other assets, then the total number of scenarios is 2 n T . {\displaystyle 2^{nT}.}

  4. Priority search tree - Wikipedia

    en.wikipedia.org/wiki/Priority_search_tree

    The priority search tree is used to store a set of 2-dimensional points ordered by priority and by a key value. This is accomplished by creating a hybrid of a priority queue and a binary search tree. The result is a tree where each node represents a point in the original dataset. The point contained by the node is the one with the lowest priority.

  5. Optimal binary search tree - Wikipedia

    en.wikipedia.org/wiki/Optimal_binary_search_tree

    The splay tree is a form of binary search tree invented in 1985 by Daniel Sleator and Robert Tarjan on which the standard search tree operations run in (⁡ ()) amortized time. [10] It is conjectured to be dynamically optimal in the required sense. That is, a splay tree is believed to perform any sufficiently long access sequence X in time O ...

  6. Scapegoat tree - Wikipedia

    en.wikipedia.org/wiki/Scapegoat_tree

    In computer science, a scapegoat tree is a self-balancing binary search tree, invented by Arne Andersson [2] in 1989 and again by Igal Galperin and Ronald L. Rivest in 1993. [1] It provides worst-case O ( log ⁡ n ) {\displaystyle {\color {Blue}O(\log n)}} lookup time (with n {\displaystyle n} as the number of entries) and O ( log ⁡ n ...

  7. Self-balancing binary search tree - Wikipedia

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

    Most operations on a binary search tree (BST) take time directly proportional to the height of the tree, so it is desirable to keep the height small. A binary tree with height h can contain at most 2 0 +2 1 +···+2 h = 2 h+1 −1 nodes. It follows that for any tree with n nodes and height h: + And that implies:

  8. Persistent data structure - Wikipedia

    en.wikipedia.org/wiki/Persistent_data_structure

    Now we have 2 versions of the tree, the original one which doesn't contain and the new tree that contains and whose root is a copy of the root of . Since copying the path from k {\displaystyle k} to T {\displaystyle T} doesn't increase the insertion time by more than a constant factor then the insertion in the persistent data structure takes O ...

  9. Monte Carlo tree search - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_tree_search

    The focus of MCTS is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space. The application of Monte Carlo tree search in games is based on many playouts, also called roll-outs. In each playout, the game is played out to the very end by selecting moves at random.