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MCQ`S For Data Structure and Algorithms 1. Suppose that we have
MCQ`S For Data Structure and Algorithms 1. Suppose that we have

Welcome to ECE 250 Algorithms and Data Structures
Welcome to ECE 250 Algorithms and Data Structures

Uses for Binary Trees…
Uses for Binary Trees…

... tree and push a pointer to it onto the stack. – If the symbol is an operator, pop two tree pointers T1 and T2 from the stack, and form a new tree whose root is the operator, and whose children are T1 and T2. – Push the new tree pointer on the stack. ...
Binary Tree
Binary Tree

Binary Search Trees
Binary Search Trees

...  Property 1: each node can have up to two successor nodes (children)  The predecessor node of a node is called its parent  The "beginning" node is called the root (no parent)  A node without children is called a leaf ...
Search, Sorting and Big
Search, Sorting and Big

スライド 1 - Researchmap
スライド 1 - Researchmap

... DFUDS Representation [6] • It encodes the degrees of nodes in unary codes in depth-first order (DFUDS = Depth First Unary Degree Sequence) ...
Cache-Oblivious Priority Queue and Graph Algorithm
Cache-Oblivious Priority Queue and Graph Algorithm

... – O(N/B) space – O(logB N+T/B) query – O(logB N) update • B-trees with elements in the leaves sometimes called B+-tree – Now B-tree and B+tree are synonyms • Construction in O( NB log M B NB ) I/Os – Sort elements and construct leaves – Build tree level-by-level bottom-up ...
Red-black tree
Red-black tree

(a,b) tree
(a,b) tree

... divided into blocks, which we call disk blocks.  The transfer of a block between external memory and primary memory is a disk transfer or I/O.  There is a great time difference that exists between main memory accesses and disk accesses  Thus, we want to minimize the number of disk transfers neede ...
Proofs, Recursion and Analysis of Algorithms
Proofs, Recursion and Analysis of Algorithms

... The depth (height) of the tree is the maximum depth of any node in the tree; in other words, it is the length of the longest path from the root to any node. A node with no children is called a leaf of the tree. All nonleaves are internal nodes. A forest is an acyclic graph (not necessarily connected ...
Doc - UCF CS
Doc - UCF CS

Scribe Notes
Scribe Notes

BINARY SEARCH TREE VISUALIZATION ALGORITHM
BINARY SEARCH TREE VISUALIZATION ALGORITHM

... From computational perspective this method is quite good, but it has a serious disadvantage. It uses a canvas in the least efficient way. If a tree is close to balanced6 or even complete7 this deficiency becomes less obvious. However, if a tree has its leaves distributed across all levels, the algor ...
Data Structures Lecture 6
Data Structures Lecture 6

... ¡  Compute space used by files in a directory and its subdirectories Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) ...
Practical Session 7
Practical Session 7

Greedy Algorithms - Ohio State Computer Science and Engineering
Greedy Algorithms - Ohio State Computer Science and Engineering

... induction hypothesis, Ai is a prefix of some optimum solution, say B = (x1 , . . . , xi , yi+1 , . . . , ym ). • If xi+1 = yi+1 , then Ai+1 is a prefix of B, and so feasible. ...
105-1 Data Structures Midterm Exam 系級: 學號: 姓名: 1. Rank the
105-1 Data Structures Midterm Exam 系級: 學號: 姓名: 1. Rank the

... has two fields – a name and a pointer to another node. For example, if John works for Mary, then the pointer of the node representing John will point to the node representing Mary. In other words, Mary is John’s boss. For ease of representation if a person does not work for anyone, we set his/her “b ...
Final Solutions
Final Solutions

Data Structures Lecture 1
Data Structures Lecture 1

CSE 326: Data Structures Lecture #7 Branching Out
CSE 326: Data Structures Lecture #7 Branching Out

CIAA2009
CIAA2009

Chapter 5-3 - Computer Science
Chapter 5-3 - Computer Science

Node
Node

doc
doc

... These lead to proposals (later in this set of notes) for (online) data structures whose adaptive behavior is asymptotically close (analyzed in Notes 3) to that of an optimal (offline) strategy. Online - must process each request before the next request is revealed. Offline - given the entire sequenc ...
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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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