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CSE 326: Data Structures Lecture #23 randomized data structures
CSE 326: Data Structures Lecture #23 randomized data structures

PPT
PPT

... A very fast overview of some data structures that we will be using this semester  lists, sets, stacks, queues, networks, trees  a variation on the well known heap data structure  binary search Illustrated using animation We are concerned with O( ) computation counts, and so do not need to get dow ...
Singly-linked List
Singly-linked List

Trees, Tree traversal
Trees, Tree traversal

... • The depth of any node is 1 more than the depth of its parent. • The height of any node is 1 more than the height of its maximum-height child. • The height of a tree is the height of the root node. • The size of a tree is the size of its root. • A tree with N nodes has N-1 edges. (Except for the ro ...
Symbol Tables - Lehigh CORAL
Symbol Tables - Lehigh CORAL

Slides
Slides

- Online Guru Jee
- Online Guru Jee

... Create, Add, Delete, Full and Empty, Circular queues, D-queues and Priority Queues. Linked list: Representation and Implementation of Singly Linked Lists, Two-way Header List, Traversing and Searching of Linked List, Overflow and Underflow, Insertion and deletion to/from Linked Lists, Insertion and ...
Slide 1
Slide 1

presentation
presentation

... each node contains, besides the next-node link, a second link field pointing to the 'previous' node in the sequence. The two links may be called 'forward('s') and 'backwards', or 'next' and 'prev'('previous'). ...
Enhancing the Linux Radix Tree
Enhancing the Linux Radix Tree

... Tags, height (shift), reference count, parent pointer, offset in parent ...
Data Structure Review
Data Structure Review

...  Instead of using pointers to the left and right child of a node, use indices into an array of nodes representing the binary tree.  Also, use variable free as an index to the first position in the array that is available for a new entry. Use either the left or right child indices to indicate addit ...
Advanced Algorithm Design and Analysis (Lecture 1)
Advanced Algorithm Design and Analysis (Lecture 1)

... • but, will not work for more dimensions… ...
Lecture 16 Student Notes
Lecture 16 Student Notes

... Figure 1: On the left we have node v with 5 children. Each triangle represents a subtree, and the size of the triangle represents the weight of the subtree (the number of descendand leaves of the subtree). On the right we can see the weight balanced BST. The solid lines are the edges of the weight ...
Notes2 - CS.Duke
Notes2 - CS.Duke

Sample questions Paper
Sample questions Paper

Chapter 16 PowerPoint
Chapter 16 PowerPoint

CS2351 Data Structures
CS2351 Data Structures

... In both cases : • The number of I/Os is O(logB n) • The number of operations is O(B logB n) • All properties of B-tree are maintained after insertion Remarks : The root is deleted when it has only one child  this child becomes new root  Tree height decreased by 1 ...
CSE143 Lecture 23: Priority Queues and HuffmanTree
CSE143 Lecture 23: Priority Queues and HuffmanTree

here
here

tree structure
tree structure

... – First, we set the appropriate link of N's parent to NIL. – We then check if all the four link fields of M are NIL. – If so, we examine M's parent (let us call it P for now). As M is P's child, we find a link field dir1 such that P.dir1 = M. We then set P.dir1 = NIL and then (as before) check to se ...
1 Deletions in 2-3 Trees
1 Deletions in 2-3 Trees

... To delete a key (and its value) from a 2-3 tree, we of course first have to find it. Once we have found the key, deleting it from an internal node would be really complicated. Therefore, in 2-3 trees (and pretty much all search trees), we always have a first step of ensuring that we only delete keys ...
ppt
ppt

Euler Tour
Euler Tour

Red Black Tree
Red Black Tree

ppt
ppt

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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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