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A link/cut tree is a data structure for representing a forest, a set of rooted trees, and offers the following operations: Add a tree consisting of a single node to the forest. Given a node in one of the trees, disconnect it (and its subtree) from the tree of which it is part. Attach a node to another node as its child.
Free lists make the allocation and deallocation operations very simple. To free a region, one would just link it to the free list. To allocate a region, one would simply remove a single region from the end of the free list and use it. If the regions are variable-sized, one may have to search for a region of large enough size, which can be ...
A top tree is a data structure based on a binary tree for unrooted dynamic trees that is used mainly for various path-related operations. It allows simple divide-and-conquer algorithms . It has since been augmented to maintain dynamically various properties of a tree such as diameter, center and median.
There are multiple straight forward approaches to implement such a trie as physical data structure. To state two: A node can be represented having an array of child pointers for each symbol of the alphabet Σ {\displaystyle \Sigma } – an array of 10 pointers per node in the decimal number example.
The interval tree data structure can be generalized to a higher dimension with identical query and construction time and () space. First, a range tree in N {\displaystyle N} dimensions is constructed that allows efficient retrieval of all intervals with beginning and end points inside the query region R {\displaystyle R} .
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.
To turn a regular search tree into an order statistic tree, the nodes of the tree need to store one additional value, which is the size of the subtree rooted at that node (i.e., the number of nodes below it).
Index structures like B-trees also reduce the number of disk accesses, and are more often used to index on-disk data in part because they can index many types of data and can be updated online. Still, interpolation search may be useful when one is forced to search certain sorted but unindexed on-disk datasets.