'Big o' diaporamas de présentation

Asymptotics

Asymptotics. Section 3.2 of Rosen Spring 2010 CSCE 235 Introduction to Discrete Structures Course web-page: cse.unl.edu/~cse235 Questions : cse235@cse.unl.edu. Outline. Introduction Asymptotic Definitions (Big O, Omega, Theta), properties Proof techniques

590 views • 38 slides

Asymptotics

Asymptotics. Section 3.2 of Rosen Spring 2012 CSCE 235 Introduction to Discrete Structures Course web-page: cse.unl.edu/~cse235 Questions : Piazza. Outline. Introduction Asymptotic Definitions (Big O, Omega, Theta), properties Proof techniques

521 views • 38 slides

Topic 11 Linked Lists

Topic 11 Linked Lists.

429 views • 33 slides

Algorithm Analysis

Algorithm Analysis. Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2010. Outline. Big O notation Two examples Search program Max. Contiguous Subsequence. Program. Algorithms + Data Structure = Programs Algorithms: Must be definite and unambiguous

566 views • 26 slides

Algorithms

Algorithms. Problems, Algorithms, Programs. Problem - a well defined task. Sort a list of numbers. Find a particular item in a list. Find a winning chess move. Algorithms. A series of precise steps, known to stop eventually, that solve a problem. NOT necessarily tied to computers.

383 views • 26 slides

CSS342: Algorithm Analysis

454 views • 25 slides

Topic 17 Fast Sorting

Topic 17 Fast Sorting. "The bubble sort seems to have nothing to recommend it, except a catchy name and the fact that it leads to some interesting theoretical problems." - Don Knuth. Previous Sorts. Insertion Sort and Selection Sort are both average case O(N 2 )

284 views • 21 slides

Shel Silverstein

1.62k views • 20 slides

Algorithmic Time Complexity Basics

Algorithmic Time Complexity Basics . Shantanu Dutt ECE Dept. UIC. Time Complexity. An algorithm time complexity is a function T(n) of problem size n that represents how much time the algorithm will take to complete its task.

398 views • 17 slides

Topic 16 Queues

"FISH queue : n.

196 views • 15 slides

Comparing Functions

Comparing Functions. Notes on Notation. N = {0,1,2,3, ... } N + = {1,2,3,4, ... } R = Set of Reals R + = Set of Positive Reals R * = R + U {0}. Comparing f ( n ) and g ( n ). Let f be a function from N to R .

378 views • 14 slides

Discrete Structures and The Three-Fold Introduction to Computer Science

231 views • 11 slides

Big O

231 views • 11 slides

Data Structues and Algorithms

210 views • 10 slides

MCA 301: Design and Analysis of Algorithms

MCA 301: Design and Analysis of Algorithms. Instructor Neelima Gupta ngupta@cs.du.ac.in. Table of Contents. About the course Mathematical Induction Asymptotic Notation. Assumption/ Pre-requisite. Some exposure to Algorithm development - at least sorting and searching

343 views • 8 slides