'O notation' presentation slideshows

Computing Fundamentals 2 Lecture 2 A theory of Graphs

Lecturer: Patrick Browne http://www.comp.dit.ie/pbrowne/ Lecture Room K408, Labs A305 & A306 Based on Chapter 19. A Logical approach to Discrete Math By David Gries and Fred B. Schneider. Computing Fundamentals 2 Lecture 2 A theory of Graphs. A Theory of Graphs.

1.89k views • 169 slides

Algorithms and Software

Algorithms and Software. Chapter 7 Some of Chapter 8 Sections 17.2, 17.3. What is an Algorithm?. An algorithm for solving a problem is: an unambiguous set of instructions that can be executed in a finite amount of time and that, when finished, has solved the problem. What a Strange Word.

1.02k views • 87 slides

Parallel Programming & Cluster Computing Applications and Types of Parallelism

Parallel Programming & Cluster Computing Applications and Types of Parallelism. Henry Neeman, Director OU Supercomputing Center for Education & Research University of Oklahoma Information Technology Oklahoma Supercomputing Symposium, Tue Oct 5 2010. Outline. Monte Carlo: Client-Server

939 views • 81 slides

CS1020 Data Structures and Algorithms I Lecture Note #9

CS1020 Data Structures and Algorithms I Lecture Note #9. Analysis of Algorithms. Objectives. References. Programs used in this lecture. TimeTest.java CompareRunningTimes1.java CompareRunningTimes2.java CompareRunningTimes3.java. Outline. What is an Algorithm ?

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Analysis of Algorithms: time & space

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SORTING AND SEARCHING

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Cmp Sci 187: Midterm Review

Cmp Sci 187: Midterm Review. Based on Lecture Notes. What Did We Cover ?. Basic Java (review) Software Design (Phone Directory) Correctness and Efficiency: Exceptions, Testing, Efficiency (Big-O) Inheritance and Class Hierarchies Lists and the Collection Interface

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The Selection Sort

518 views • 32 slides

Algorithm Analysis

Algorithm Analysis. Dr. Bernard Chen Ph.D. University of Central Arkansas. Outline. Big O notation Two examples Search program Max. Contiguous Subsequence. Program. Algorithms + Data Structure = Programs Algorithms: Must be definite and unambiguous

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What is CS 253 about?

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COS 433: Cryptography

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Discrete Mathematics CS 2610

Discrete Mathematics CS 2610. February 26, 2009 -- part 1. Big-O Notation. Big-O notation is used to express the time complexity of an algorithm We can assume that any operation requires the same amount of time.

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Assignment 2: (Due at 10:30 a.m on Friday of Week 10)

Question 1 (Given in Tutorial 5) Question 2 (Given in Tutorial 7) If you do Question 1 only, you get 60 points. If you do Question 2 only, you get 90 points. If you correctly do both Question 1 and Question 2, you get 100 points.

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CSE1301 Computer Programming: Lecture 35 Revision 1

CSE1301 Computer Programming: Lecture 35 Revision 1 Topics Type of questions What do you need to know? About the exam Exam technique Staff consultation Revision Drop-in Center Sample questions Types of Questions Tutorial Sheets

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Lecture 2: Algorithm Complexity

Lecture 2: Algorithm Complexity. Recursion. A subroutine which calls itself, with different parameters. Need to evaluate factorial(n). factorial(n) = n ?factorial(n-1). Suppose routine factorial(p) can find factorial of p for all p < m. Then factorial(m+1) can be solved as follows:

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Discrete Mathematics CS 2610

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The Insertion Sort

253 views • 14 slides

Computational Complexity Of The Bellman-Ford Algorithm

Computational Complexity Of The Bellman-Ford Algorithm. Alexander c. nwala Introduction to Networks and Communications Dr. Kurt maly Department of computer science Old dominion university. Complexity Of The Bellman-Ford::premise.

406 views • 9 slides