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p - CS1001.py
p - CS1001.py

... • We cannot use binary search to look for an element in an ordered list. • This is because random access to the i’th element is not possible in constant time in linked lists (as opposed to arrays such as Python’s lists). ...
InOrder Traversal Algorithm
InOrder Traversal Algorithm

... All the nodes in the left subtree of v contain items less than the item in v and All the nodes in the right subtree of v contain items greater than or equal to the item in v ...
The ADT Heap
The ADT Heap

linear list Concept:
linear list Concept:

... a singly linked list. Even if we have a tail reference directly to the last node of the list, we must be able to access the node before the last node in order to remove the last node. But we can not reach the node before the tail by following next links from the tail. The only way to access this nod ...
Document
Document

The Random Access Zipper: Simple, Purely
The Random Access Zipper: Simple, Purely

Lists - Dr. Manal Helal Moodle Site
Lists - Dr. Manal Helal Moodle Site

... Recursion can be used to perform operations on a linked list In a circular linked list, the last node points to the first node Dummy head nodes eliminate the special cases for insertion into and deletion from the beginning of a linked list ...
CHAPTER 7 BINARY TREES What is a Tree?
CHAPTER 7 BINARY TREES What is a Tree?

Heaps and Priority Queues
Heaps and Priority Queues

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L-1

Skew Heaps
Skew Heaps

Linked list resources
Linked list resources

... How do we know where a list begins and ends? We use a special value called null, the null determines the start and end. The reference part of the last node always points to the null. To determine where the list starts There is an external reference to the beginning of the list. This is known as the ...
[MS Word]
[MS Word]

Index Structures for Files Multi-Level Indexes Multi
Index Structures for Files Multi-Level Indexes Multi

... • The binary search does not find K, so Pos is set to the index of the pointer between I and M, that is Node.pointers[0]. • The value of Node.pointers[0] is NULL. There is no sub-tree which contains keys between D and N. • The function search is called again with parameters of NodePtr = ...
Self-Adjusting Binary Search Trees
Self-Adjusting Binary Search Trees

CSC 263 Lecture 1
CSC 263 Lecture 1

... Now, Delete ( BST root R, node x ) has three cases: 1. If x has no children, simply remove it by setting x to null. 2. If x has one child y and z is the parent of x, then we remove x and make y the appropriate child of z (i.e. the left child if x was the left child of z and the right child if x was ...
Solid Modeling 3D Object Representations • Raw data • Solids
Solid Modeling 3D Object Representations • Raw data • Solids

Balanced Search Trees
Balanced Search Trees

... • Placing data items in nodes of a 2-3 tree – A 2-node (has two children) must contain single data item greater than left child’s item(s) and less than right child’s item(s) – A 3-node (has three children) must contain two data items, S and L , such that • S is greater than left child’s item(s) and ...
27. Spatial access methods
27. Spatial access methods

... whether the stored node (split point) is included in the search range and whether there is overlap with the left or right subtree. For each subtree which overlaps the search region, the procedure is repeated until the leaf level is reached. A disadvantage of the KD-tree is that the shape of the tree ...
Worst Case Efficient Data Structures for Priority Queues and Deques
Worst Case Efficient Data Structures for Priority Queues and Deques

... Real time systems In real time systems, the most valuable properties a system component can have are predictability and speed. Amortized algorithms are not predictable, unless we assume that every operation takes as long as any other operation in the sequence and allocate time accordingly. This can ...
LINKED DATA STRUCTURES
LINKED DATA STRUCTURES

... Linked Lists vs Arrays Arrays are contiguous • In an array, the elements have to be in a contiguous (connected and sequential) portion of memory. • Memory immediately next to the array may already be in use for something else. • So programming languages don’t generally provide for arrays that can g ...
Concurrency and Recovery in Generalized Search Trees
Concurrency and Recovery in Generalized Search Trees

Elementary Data Structures: Binary Search Trees
Elementary Data Structures: Binary Search Trees

QUERY-ANSWERING*
QUERY-ANSWERING*

Non Linear Data Structure
Non Linear Data Structure

... (a) The key in the left child of a node (if any) preceeds the key in the parent node. (b) The key in the right child of a node (if any) succeeds the key in the parent node. (c) The left and right subtree of the root are again binary search trees. Here, we are assuming that there are no duplicates wh ...
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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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