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Intuition for Asymptotic Notation

Intuition for Asymptotic Notation

Intuition for Asymptotic Notation. Big-Oh f(n) is O(g(n)) if f(n) is asymptotically less than or equal to g(n) big-Omega f(n) is ?(g(n)) if f(n) is asymptotically greater than or equal to g(n) big-Theta f(n) is ?(g(n)) if f(n) is asymptotically equal to g(n) little-oh

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1.41k views • 85 slides
G64ADS Advanced Data Structures

G64ADS Advanced Data Structures

G64ADS Advanced Data Structures. Priority Queue. Why Priority Queue. Queues are a standard mechanism for ordering tasks on a first-come, first-served basis However, some tasks may be more important or timely than others (higher priority) Priority queues

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633 views • 51 slides
Problem of the Day

Problem of the Day

Problem of the Day. To get into club, must get past bouncer Recorded correct responses of those before you Guard said five & response was four Guard said three & response was five Guard said twelve & response was six Guard said six & response was three

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579 views • 49 slides
CSE373: Data Structures & Algorithms Lecture 9 : Priority Queues

CSE373: Data Structures & Algorithms Lecture 9 : Priority Queues

CSE373: Data Structures & Algorithms Lecture 9 : Priority Queues. Aaron Bauer Winter 2014. Midterm. On Wednesday, in class Closed book C losed note Closed electronic devices Closed classmate Covers everything up through priority queues and binary heaps does not include AVL tree delete

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782 views • 46 slides
Chapter 2: Basic Data Structures

Chapter 2: Basic Data Structures

Chapter 2: Basic Data Structures. Basic Data Structures. Stacks Queues Vectors, Linked Lists Trees (Including Balanced Trees) Priority Queues and Heaps Dictionaries and Hash Tables. Two Definitions. Depth of a node v in a tree is: 0 if v is root, else 1 + depth of parent of v

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592 views • 44 slides
Heaps and Priority Queues

Heaps and Priority Queues

2. 5. 6. 9. 7. Heaps and Priority Queues. A priority queue stores a collection of items An item is a pair (key, element) Main methods of the Priority Queue ADT insertItem (k, o) inserts an item with key k and element o removeMin () removes the item with the smallest key.

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519 views • 39 slides
CMSC 341

CMSC 341

CMSC 341. Binary Heaps Priority Queues. Priority Queues. Priority: some property of an object that allows it to be prioritized with respect to other objects of the same type

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544 views • 38 slides
CMSC 341

CMSC 341

CMSC 341. Binary Heaps Priority Queues. Priority Queues. Priority: some property of an object that allows it to be prioritized with respect to other objects of the same type

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483 views • 38 slides
Binary Heaps

Binary Heaps

Binary Heaps. COL 106 Shweta Agrawal and Amit Kumar. Revisiting FindMin. Application: Find the smallest ( or highest priority) item quickly Operating system needs to schedule jobs according to priority instead of FIFO

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432 views • 34 slides
CMSC 341

CMSC 341

CMSC 341. Binary Heaps Priority Queues. Priority Queues. Priority: some property of an object that allows it to be prioritized WRT other objects (of the same type) Priority Queue: homogeneous collection of Comparables with the following operations (duplicates are allowed)

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411 views • 30 slides
A priority queue stores a collection of entries Each entry is a pair (key, value)

A priority queue stores a collection of entries Each entry is a pair (key, value)

Priority Queue. A priority queue stores a collection of entries Each entry is a pair (key, value) Main methods of the Priority Queue insert(k, x) : inserts an entry with key k and value x removeMin() : removes and returns the entry with smallest key Additional methods

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329 views • 24 slides
Two-tier relaxed heaps

Two-tier relaxed heaps

Two-tier relaxed heaps. Presented by Claus Jensen Joint work with Amr Elmasry and Jyrki Katajainen Slides available at www.cphstl.dk. Heaps. Our focus has been on the worst-case comparison complexity of the heap operations: Find-min Insert Decrease(-key) Delete

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332 views • 23 slides
Heaps

Heaps

Heaps. 2. 5. 6. 9. 7. A priority queue stores a collection of entries Each entry is a pair (key, value) Main methods of the Priority Queue ADT insert (k, x) inserts an entry with key k and value x removeMin () removes and returns the entry with smallest key. Additional methods

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353 views • 20 slides
Heaps

Heaps

Heaps. 2. 5. 6. 9. 7. A priority queue stores a collection of entries Each entry is a pair (key, value) Main methods of the Priority Queue ADT insert (k, x) inserts an entry with key k and value x removeMin () removes and returns the entry with smallest key. Additional methods

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339 views • 20 slides
Priority Queues and Heaps

Priority Queues and Heaps

Priority Queues and Heaps. 2. 5. 6. 9. 7. A priority queue stores a collection of entries Typically, an entry is a pair (key, value), where the key indicates the priority Main methods of the Priority Queue ADT insert (e) inserts an entry e

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315 views • 20 slides
Heaps

Heaps

Heaps. 2. 5. 6. 9. 7. A priority queue stores a collection of entries Each entry is a pair (key, value) Main methods of the Priority Queue ADT insert (k, x) inserts an entry with key k and value x removeMin () removes and returns the entry with smallest key. Additional methods

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317 views • 17 slides
Heaps (8.3)

Heaps (8.3)

Heaps (8.3). CSE 2011 Winter 2011. Priority Queues. Data structure supporting the following operations: insert (equivalent to enqueue) deleteMin (or deleteMax ) (equivalent to dequeue) Other operations (optional) Applications: Emergency room waiting list

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238 views • 16 slides
Priority Queues

Priority Queues

Priority Queues. MakeQueue create new empty queue Insert ( Q , k , p ) insert key k with priority p Delete ( Q, k ) delete key k (given a pointer) DeleteMin (Q) delete key with min priority Meld( Q 1 , Q 2 ) merge two sets Empty ( Q ) returns if empty Size ( Q ) returns # keys

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207 views • 11 slides
Heaps

Heaps

Heaps. 2. 5. 6. 9. 7. A heap is a binary tree storing keys at its nodes and satisfying the following properties: Heap-Order: for every internal node v other than the root, key ( v ) ? key ( parent ( v )) Complete Binary Tree: let h be the height of the heap

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211 views • 10 slides
Review for Final Exam

Review for Final Exam

Review for Final Exam. Non-cumulative, covers material since exam 2 Data structures covered: Treaps Skip lists Hash tables Disjoint sets Graphs For each of these data structures Basic idea of data structure and operations Be able to work out small example problems Prove related theorems

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230 views • 7 slides

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