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In computer science, a priority search tree is a tree data structure for storing points in two dimensions. It was originally introduced by Edward M. McCreight. [1] It is effectively an extension of the priority queue with the purpose of improving the search time from O(n) to O(s + log n) time, where n is the number of points in the tree and s is the number of points returned by the search.
This unsorted tree has non-unique values (e.g., the value 2 existing in different nodes, not in a single node only) and is non-binary (only up to two children nodes per parent node in a binary tree). The root node at the top (with the value 2 here), has no parent as it is the highest in the tree hierarchy.
An augmented tree can be built from a simple ordered tree, for example a binary search tree or self-balancing binary search tree, ordered by the 'low' values of the intervals. An extra annotation is then added to every node, recording the maximum upper value among all the intervals from this node down.
In computer science, tree traversal (also known as tree search and walking the tree) is a form of graph traversal and refers to the process of visiting (e.g. retrieving, updating, or deleting) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited.
Minimum degree spanning tree; Minimum k-cut; Minimum k-spanning tree; Minor testing (checking whether an input graph contains an input graph as a minor); the same holds with topological minors; Steiner tree, or Minimum spanning tree for a subset of the vertices of a graph. [2] (The minimum spanning tree for an entire graph is solvable in ...
In computer science, a trie (/ ˈ t r aɪ /, / ˈ t r iː /), also known as a digital tree or prefix tree, [1] is a specialized search tree data structure used to store and retrieve strings from a dictionary or set. Unlike a binary search tree, nodes in a trie do not store their associated key.
A query for a segment tree receives a point q x (should be one of the leaves of tree), and retrieves a list of all the segments stored which contain the point q x. Formally stated; given a node (subtree) v and a query point q x, the query can be done using the following algorithm: Report all the intervals in I(v). If v is not a leaf:
Inserting an item with key k appends the item to the k 'th list, and updates top ← min(top, k), both in constant time. Extract-min deletes and returns one item from the list with index top, then increments top if needed until it again points to a non-empty list; this takes O(C) time in the worst case. These queues are useful for sorting the ...