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Lecture 1 Student Notes
Lecture 1 Student Notes

... We have vaguely referred to persistence as the ability to answer queries about the past states of the structure. Here we give several definitions of what we might mean by persistence. 1. Partial Persistence – In this persistence model we may query any previous version of the data structure, but we m ...
Finger trees: a simple general
Finger trees: a simple general

... We shall now proceed to show that finger trees make efficient deques, with all operations taking Θ(1) amortized time. This holds even if the structure is used in a persistent manner, where several versions of a tree may coexist (Driscoll et al., 1989). The analysis is essentially the same as that fo ...
A Space-Efficient Algorithm for Segment Intersection
A Space-Efficient Algorithm for Segment Intersection

... given in an array; algorithms can modify the array but are allowed only a small amount of extra memory (usually O(1) or O(polylog n)). In the “non-destructive” model, we insist further that the final array holds a permutation of the original input elements. Such spaceefficient algorithms are analogo ...
Binary Search Trees - University of Calgary
Binary Search Trees - University of Calgary

... Need techniques to ensure that all trees are close to full want h ∈ Θ(log n) in the worst case one possibility: red-black trees (next three lectures) Mike Jacobson (University of Calgary) ...
Parallel Euler tour and Post Ordering for Parallel Tree Accumulations
Parallel Euler tour and Post Ordering for Parallel Tree Accumulations

an r-tree node splitting algorithm using mbr partition for spatial query
an r-tree node splitting algorithm using mbr partition for spatial query

Binary Search Trees - University of Calgary
Binary Search Trees - University of Calgary

... complexity Θ(n) all nodes have exactly one child (i.e., tree only has one leaf) Eg. will occur if elements are inserted into the tree in ascending (or descending) order Data Structures & Algorithms in Java (Lafore) Chapter 8 Discusses in more detail, including algorithms for tree traversals ...
Lecture 9 — 16 Feb, 2012 1 Overview 2 The problem
Lecture 9 — 16 Feb, 2012 1 Overview 2 The problem

... complexity of each operation. Again, we consider the elements as log u length binary vectors. For each vector in S and each i ∈ [log u], we define Si = {v : |v| = i, ∃x ∈ S, x(i) = v}, where x(i) represents the vector restricted to the most significant i positions . For each Si we build a hash table ...
(iii) Data Structure with Algorithm
(iii) Data Structure with Algorithm

... If N has one child, check whether it is a right or left child. If it is a right child, then find the smallest element from the corresponding right sub tree. Then replace the smallest node information with the deleted node. If N has a left child, find the largest element from the corresponding left s ...
20Tall
20Tall

... The left child of the root (referenced by A) has a value (5) that is less than the value of the root (8). Likewise, the value of the right child of the root has a value (10) that is greater than the root’s value (8). Also, all the values in the subtree referenced by A (4, 5, 7), are less than the va ...
3. Differentiate internal and external nodes of a binary tree.
3. Differentiate internal and external nodes of a binary tree.

... else return TREE-SEARCH (right[x], k) 2.6.1.3 Inserting an element Both insertion and deletion operations cause changes in the data structure of the dynamic set represented by a binary search tree. For a standard insertion operation, we only need to search for the proper position that the element sh ...
Program Design Including Data Structures, Fifth Edition
Program Design Including Data Structures, Fifth Edition

PPT printable - Simpson College
PPT printable - Simpson College

child
child

... Consider the set of formulas from variables x1, x2, ..., xn and operators ^, V and ~ The value of a variable is either TRUE or FALSE. The expression is defined as : (1) A variable is an expression (2) If x and y are expressions, then ~x, x^y, x v y are expressions (3) Parenthesis can be used to alte ...
Purely Functional Worst Case Constant Time Catenable Sorted Lists
Purely Functional Worst Case Constant Time Catenable Sorted Lists

... w(Ti ) ≥ w(Tj ). In this case, tree Tj is attached to tree Ti , and the result of this operation is the tree Ti . Tj is attached to a node on the spine of Ti . There exist various implementations of biased trees differing in the used balance criterion; our construction is based on the biased 2, b t ...
Ch 12 Collections
Ch 12 Collections

Lecture Note 05 EECS 4101/5101 Instructor: Andy Mirzaian SKEW
Lecture Note 05 EECS 4101/5101 Instructor: Andy Mirzaian SKEW

... key, denoted key(x) and two pointers left(x) and right(x), to its left child and right child, respectively. If x has no left child we define left(x) = Λ; if x has no right child we define right(x) = Λ. Access to the tree is by a pointer to its root; we represent an empty tree by a pointer to Λ. With ...
downoad
downoad

Cache-Oblivious Priority Queue and Graph Algorithm
Cache-Oblivious Priority Queue and Graph Algorithm

binary search tree
binary search tree

binary search tree
binary search tree

... a tree is a nonlinear data structure consisting of nodes (structures containing data) and edges (connections between nodes), such that:  one node, the root, has no parent (node connected from above)  every other node has exactly one parent node  there is a unique path from the root to each node ( ...
Lec08c2-Linked List and Exercise 1
Lec08c2-Linked List and Exercise 1

... IntNodePtr search(IntNodePtr head, int target); //Precondition: pointer head points to head of //linked list. Pointer in last node is NULL. //If list is empty, head is NULL //Returns pointer to 1st node containing target //If not found, returns NULL ...
Amortized Analysis - Carleton University
Amortized Analysis - Carleton University

... that occurs during the sequence of increment operations. Proof. Consider a bit-change in which a 0 is changed into a 1. By our payment scheme, we pay the one dollar for this bit-change. In fact, we pay two dollars, but the other one is put into the account. Consider a bit-change in which a 1 is chan ...
ppt
ppt

Concurrent Search Tree by Lazy Splaying
Concurrent Search Tree by Lazy Splaying

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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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