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Notes (part 1)
Notes (part 1)

Data Structures So Far
Data Structures So Far

...  remove(e): Remove from P and return entry e.  replaceKey(e,k): Replace with k and return the old key; an error condition occurs if k is invalid (that is, k cannot be compared with other keys).  replaceValue(e,x): Replace with x and return the old ...
Fully persistent lists with catenation
Fully persistent lists with catenation

Chapter 2: Using Objects
Chapter 2: Using Objects

... Binary Search Trees (BSTs) ...
lecture22
lecture22

Using and storing the index Review:
Using and storing the index Review:

Slide 1
Slide 1

Dual-Sorted Inverted Lists *
Dual-Sorted Inverted Lists *

PowerPoint
PowerPoint

A Data Structure for Manipulating Priority Queues (by Jean Vuillemin
A Data Structure for Manipulating Priority Queues (by Jean Vuillemin

... refer to this operation as couplings, and the general UNION procedure is a sequence of couplings. For treating the general case where n and n' may be arbitrary, it is convenient to use an analogy with the ordinary scheme for the binary addition of n and n'. The UNION proceeds from low order bits to ...
Data Structures - Test 1 Ο
Data Structures - Test 1 Ο

Range Searching
Range Searching

previous-qns-and-answers
previous-qns-and-answers

Lower  Bounds for  Orthogonal  Range Searching:
Lower Bounds for Orthogonal Range Searching:

pptx
pptx

Linear Lists
Linear Lists

... Insert at Beginning •A new node is added before the first node of the list. •Determine that addition is at the beginning of the list. If the predecessor pointer is a null pointer, there is no predecessor, so we are at the beginning of the list. •Point the new node to the first node of the list and ...
Richard Tarjent
Richard Tarjent

... this problem has already come up several times in some of the talks in the conference “Second Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics and Algorithms.” The goal is to maintain a collection of n elements which are partitioned into sets, i.e., the sets are always ...
Upgraded Tango Tree to solve the Dictionary Problem and its
Upgraded Tango Tree to solve the Dictionary Problem and its

Binary Trees
Binary Trees

... • Theorem: Let T be a binary search tree with n nodes, where n > 0.The average number of nodes visited in a search of T is approximately 1.39log2n • Number of comparisons required to determine whether x is in T is one more than the number of comparisons required to insert x in T • Number of comparis ...
Sandhya Dasu
Sandhya Dasu

BFS Spanning Tree
BFS Spanning Tree

9781423902225_IM_ch20
9781423902225_IM_ch20

2-3-4 Trees
2-3-4 Trees

... This means that there are 0-3 data items in a node. The number of links is referred to as the order of the tree The links may have to move depending on insertions or deletions to the data in the node ...
Trees and Searching - Doc Dingle Website
Trees and Searching - Doc Dingle Website

Chapter 7: B
Chapter 7: B

... disk, this size would be the size of one page of disk memory. When a B-Tree is used to implement a Map, the items in the tree are key/value pairs. As a variation on this, the values can be stored outside the tree, with the tree containing pointers to the values. That is, an item in the tree would co ...
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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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