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Trees
Trees

Slides for Exam 3 review
Slides for Exam 3 review

Red-Black tree
Red-Black tree

... memory. However, remember word size and memory. Note this works both ways: a single bit could mean the incursion of an extra cost. However, a node may well have excess space because it doesn't take up a full word size and so no cost to store the extra bit. ...
Slides
Slides

Data Structure
Data Structure

... makes searching easier. This corresponds to the records that shall be stored in leaf nodes. 27. Draw the B-tree of order 3 created by inserting the following data arriving in sequence – 92 24 6 7 11 8 22 4 5 16 19 20 78 ...
Trees
Trees

...  The implementation of Positions for binary trees in net.datastructures is a bit subtle.  BTPosition is an interface in net.datastructures that represents the positions of a binary tree. This is used extensively to define the types of objects used by the LinkedBinaryTree class that LinkedBin ...
Trees - GearBox
Trees - GearBox

... ► Go to parent or children from a given node ► Add a root to an empty tree ► Add a child to a node ► Remove a node (can impose that the node be a leaf, for simplicity) ► Get the element associated to a node ► Replace the element associated to a node ...
Document
Document

Heaps - WordPress.com
Heaps - WordPress.com

... A very efficient way to implement a priority queue is with a heap ordered by priority each node is higher priority than everything below it. The highest priority element, then, is at the top of the heap. We've just seen that the top element can be retrieved with a single delete operation - O(logN) - ...
Podcast Ch16b
Podcast Ch16b

... node. Let TN be the subtree with root N and TL and TR be the roots of the left and right subtrees of N. Then height(N) = height(TN) = ...
Binary Trees 1
Binary Trees 1

... The general binary tree shown in the previous chapter is not terribly useful in practice. The chief use of binary trees is for providing rapid access to data (indexing, if you will) and the general binary tree does not have good performance. Suppose that we wish to store data elements that contain a ...
25-btrees
25-btrees

Amortized Analysis Master MOSIG
Amortized Analysis Master MOSIG

... 5. Suppose that we need to rebalance the subtree rooted at node x ∈ T . Let m = size[x]. Show that δ(x) ≥ (2α − 1)m + 1. 6. Let Φbef ore the potential in the tree just before we rebalance the subtree rooted at node x and Φaf ter the potential in the tree just after. Show that Φbef ore − Φaf ter ≥ k ...
Dictionary
Dictionary

... so insertion only needs to be done at the lowest unbalanced ancestor. • To delete an element, we: • Delete the element (as per usual for a binary search tree). • Starting with its parent, check each ancestor of the new node to make sure  it’s balanced. • If any node is not balanced, perform the nece ...
ICS 220 – Data Structures and Algorithms
ICS 220 – Data Structures and Algorithms

... • Therefore, it is clear that for indexes to be appropriate to guide towards correct data, they needn’t actually be the values stored in the leaves. – A Prefix B+ Tree stores just a prefix to the data stored in a leaf in the index nodes. – For instance 4 or AB. – This is similar to the keyword at th ...
Lecture of Week 4
Lecture of Week 4

... children. Leaf nodes have no children, and so their ci attributes are undefined. The keys x.keyi separate the ranges of keys stored in each subtree: if ki is any key stored in the subtree with root x.ci, then k1 <= x.key1 <= k2 <= x.key2 <= … <= X.keyx.n <= kx.n+1 All leaves have the same depth, whi ...
CSC401: Analysis of Algorithms
CSC401: Analysis of Algorithms

HONR280
HONR280

Representing Trees Introduction Trees and representations
Representing Trees Introduction Trees and representations

... Are there more sources for economising on space without sacrificing speed? Yes – although not unconditionally – and the way this can be done leads to an interesting observation regarding the role of hierarchy as a data structuring pattern in general. As already mentioned, in representing trees – and ...
Midterm (with solution)
Midterm (with solution)

binary search tree
binary search tree

Tree - UMass CS !EdLab
Tree - UMass CS !EdLab

... Create a tree Determine if a tree is empty Empty a tree Set and Get the element at the root Get the left and right subtrees at the root Create a left and right subtree for the root Attach a tree as the left or right subtree of the root Detach the left or right subtree of the root ...
Lecture 1
Lecture 1

EE 461_Data Structures
EE 461_Data Structures

Week 5 Precept COS 226 Data Structures and Algorithms Computer Science Department
Week 5 Precept COS 226 Data Structures and Algorithms Computer Science Department

< 1 ... 65 66 67 68 69 70 71 72 73 ... 76 >

Interval tree

In computer science, an interval tree is a tree data structure to hold intervals. Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point. It is often used for windowing queries, for instance, to find all roads on a computerized map inside a rectangular viewport, or to find all visible elements inside a three-dimensional scene. A similar data structure is the segment tree.The trivial solution is to visit each interval and test whether it intersects the given point or interval, which requires O(n) time, where n is the number of intervals in the collection. Since a query may return all intervals, for example if the query is a large interval intersecting all intervals in the collection, this is asymptotically optimal; however, we can do better by considering output-sensitive algorithms, where the runtime is expressed in terms of m, the number of intervals produced by the query. Interval trees have a query time of O(log n + m) and an initial creation time of O(n log n), while limiting memory consumption to O(n). After creation, interval trees may be dynamic, allowing efficient insertion and deletion of an interval in O(log n). If the endpoints of intervals are within a small integer range (e.g., in the range [1,...,O(n)]), faster data structures exist with preprocessing time O(n) and query time O(1+m) for reporting m intervals containing a given query point.
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