'Asymptotic complexity' diaporamas de présentation

CS 2110 Final Review

CS 2110 Final Review. Topics. Will be covered Important Programming Concepts Types, Recursion/Induction, Asymptotic Complexity The Toolbox (Data Structures) Arrays, Linked Lists, Trees (BSTs, Heaps), Hashtables Practical Things Searching, Sorting, Abstract Data Types Graphs

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A Heap Is Efficiently Represented As An Array

9. 8. 7. 6. 7. 2. 6. 5. 1. A Heap Is Efficiently Represented As An Array. 9. 8. 7. 6. 7. 2. 6. 5. 1. 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 1. 9. 2. 3. 8. 7. 4. 7. 5. 6. 6. 7. 2. 6. 5. 1. 8. 9. Moving Up And Down A Heap. 9. 8. 7. 6. 7. 2. 6. 5. 1.

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Program Optimization Adapted from CMU course 15-213

Program Optimization Adapted from CMU course 15-213. Instructors: Yuan Tang. Today. Overview Generally Useful Optimizations Code motion/ precomputation Strength reduction Sharing of common subexpressions Removing unnecessary procedure calls Optimization Blockers Procedure calls

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Chapter 2 Complexity Analysis

Chapter 2 Complexity Analysis. Objectives. Discuss the following topics: Computational and Asymptotic Complexity Big-O Notation Properties of Big-O Notation ? and ? Notations Examples of Complexities Finding Asymptotic Complexity: Examples Amortized Complexity

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Pushpak Bhattacharyya CSE Dept., IIT Bombay 22 nd March, 2011

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Ready-For-Use: 3 Weeks of Parallelism and Concurrency in a Required 2 nd -Year Data-Structures Course

Ready-For-Use: 3 Weeks of Parallelism and Concurrency in a Required 2 nd -Year Data-Structures Course. Dan Grossman University of Washington 2010 Workshop on Curricula for Concurrency and Parallelism. Executive Summary. 2.5 week unit introducing threads + shared memory

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Ready-For-Use: 3 Weeks of Parallelism and Concurrency in a Required 2 nd -Year Data-Structures Course

Ready-For-Use: 3 Weeks of Parallelism and Concurrency in a Required 2 nd -Year Data-Structures Course. Dan Grossman University of Washington 2010 Workshop on Curricula for Concurrency and Parallelism. Executive Summary. 2.5 week unit introducing threads + shared memory

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Introduction to Algorithms Analysis

Introduction to Algorithms Analysis. CSE 680 Prof. Roger Crawfis. Run-time Analysis. Depends on input size input quality (partially ordered) Kinds of analysis Worst case (standard) Average case (sometimes) Best case (never). What do we mean by Analysis?.

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Algorithmic Complexity 2

Algorithmic Complexity 2. Nelson Padua-Perez Chau-Wen Tseng Department of Computer Science University of Maryland, College Park. Overview. Critical sections Recurrence relations Comparing complexity Types of complexity analysis. Analyzing Algorithms. Goal

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Complexity Analysis

Complexity Analysis. Asymptotic Complexity Big-O (asymptotic) Notation Big-O Computation Rules Proving Big-O Complexity How to determine complexity of code structures. Asymptotic Complexity. Finding the exact complexity, f(n) = number of basic operations , of an algorithm is difficult.

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Introduction in Computer Science 2 Asymptotic Complexity

Introduction in Computer Science 2 Asymptotic Complexity. DEEDS Group - TU Darmstadt Prof. Neeraj Suri Constantin Sarbu Brahim Ayari Dan Dobre Abdelmajid Khelil. Remember: Sequential Search. Given: Array A of integers and a constant c. Question : Is c in A?. Memory Complexity (in Java)

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Dan Grossman Last Updated : November 2012

A Sophomoric Introduction to Shared-Memory Parallelism and Concurrency Lecture 3 Parallel Prefix, Pack, and Sorting. Dan Grossman Last Updated : November 2012 For more information, see http://www.cs.washington.edu/homes/djg/teachingMaterials/. Outline. Done:

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NITRO : A Framework for Adaptive Code Variant Tuning

NITRO : A Framework for Adaptive Code Variant Tuning. Saurav Muralidharan , Manu Shantharam , Mary Hall, Michael Garland*, Bryan Catanzaro* University of Utah and *NVIDIA Research. Disclaimers.

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Prelim Question

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CSE373: Data Structures and Algorithms Lecture 4 : Asymptotic Analysis

CSE373: Data Structures and Algorithms Lecture 4 : Asymptotic Analysis. Aaron Bauer Winter 2014. Previously, on CSE 373. We want to analyze algorithms for efficiency (in time and space) And do so generally and rigorously not timing an implementation

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Data Structures by R.S. Chang, Dept. CSIE, NDHU

Chapter 4 Complexity Analysis. 4.1 Computational and Asymptotic Complexity. The same problem can frequently be solved with different algorithms which differ in efficiency.

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Complexity Analysis (Part II )

Complexity Analysis (Part II ). Asymptotic Complexity Big-O (asymptotic) Notation Big-O Computation Rules Proving Big-O Complexity How to determine complexity of code structures. Asymptotic Complexity.

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EECS 598: Background in Theory of Computation

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Complexity Analysis (Part II )

Complexity Analysis (Part II ). Asymptotic Complexity Big-O (asymptotic) Notation Big-O Computation Rules Proving Big-O Complexity How to determine complexity of code structures. Asymptotic Complexity.

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269202 Algorithms for iSNE

269202 Algorithms for iSNE. Dr. Kenneth Cosh Week 1. Course Description.

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