'Average case analysis' diaporamas de présentation

CS420 lecture one Problems, algorithms, decidability, tractability

CS420 lecture one Problems, algorithms, decidability, tractability. Problems, solutions, algorithms. In this course we study questions such as: given a problem, how do we find an (efficient) algorithm solving it? How do we measure the complexity (time, space requirements) of an algorithm.

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Amortized Analysis

Amortized Analysis. Some of these lecture slides are adapted from CLRS. Beyond Worst Case Analysis. Worst-case analysis. Analyze running time as function of worst input of a given size. Average case analysis. Analyze average running time over some distribution of inputs. Ex: quicksort.

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CSE 830: Design and Theory of Algorithms

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Data Structures

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Chapter 2 The Fundamentals: Algorithms, the Integers, and Matrices

Chapter 2 The Fundamentals: Algorithms, the Integers, and Matrices. Algorithm : specifying a sequence of steps used to solve a problem. Computational complexity of an algorithm : what are the computer resources needed to use this algorithm to solve a problem of a specified size?

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Kolmogorov complexity and its applications

CS 898 Spring, 2014. Kolmogorov complexity and its applications. Ming Li School of Computer Science University of Waterloo http://www.cs.uwaterloo.ca/~mli/cs898.html.

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Kolmogorov complexity and its applications

CS860, Winter, 2010. Kolmogorov complexity and its applications. Ming Li School of Computer Science University of Waterloo http://www.cs.uwaterloo.ca/~mli/cs860.html.

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Analysis of Algorithms

Analysis of Algorithms. The Non-recursive Case. Except as otherwise noted, the content of this presentation is licensed under the Creative Commons Attribution 2.5 License. . Key Topics:. Introduction Generalizing Running Time Doing a Timing Analysis Big-Oh Notation Big-Oh Operations

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O Notation Examples

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Quicksort

COMP171 Fall 2005. Quicksort. Introduction. Fastest known sorting algorithm in practice Average case: O(N log N) Worst case: O(N 2 ) But, the worst case seldom happens. Another divide-and-conquer recursive algorithm like mergesort. Quicksort. S. Divide step:

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Discrete Mathematics CS 2610

Discrete Mathematics CS 2610. September 23, 2008. Algorithms. An Algorithm is a finite set of precise instructions for performing a computation or for solving a problem. Properties of an algorithm: - input: input values are from a specified set

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Course : Algorithms in the Real World (296.3) Instructor : Bruce Maggs

Course : Algorithms in the Real World (296.3) Instructor : Bruce Maggs Based on a course developed at Carnegie Mellon primarily by Guy Blelloch URL : www.cs.duke.edu/courses/spring10/cps296.3. Expected Background. Asymptotic analysis Graph Terminology and Algorithms

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Tips for Research A Personal Perspective

Tips for Research A Personal Perspective. Honours Survival Course Tadao Takaoka . Tips. Standing on the shoulder of a giant Generalization Specialization Horizontal Shift Competition and curiosity Fundamental change in definition Average case analysis and parallelization.

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Understanding Simulators Using Tree-Query Language

Understanding Simulators Using Tree-Query Language. New idea: Treatment learners. Tim Menzies 1 , Ying Hu 1 , James D. Kipers 2 1 SE, ECE, UBC, Canada 2 Com Sci, Miami, Oxford, Ohio, USA tim@menzies.com , huying_ca@yahoo.com , kiperjd@muohio.edu. Business motivation.

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Lecture 5. The Incompressibility Method

Lecture 5. The Incompressibility Method. A key problem in computer science: analyze the average case performance of a program. Using the Incompressibility Method : Give the program a random input (with high Kolmogorov complexity)

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Lecture 5. The Incompressibility Method

Lecture 5. The Incompressibility Method. A key problem in computer science: analyze the average case performance of a program. Using the Incompressibility Method : Give the program a random input (with high Kolmogorov complexity)

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CS5 3 21 Numerical Optimization

CS5 3 21 Numerical Optimization. 13 Linear Programming: The Simplex Method. The standard form. The standard form of linear programming is Min x z=c T x subject to Ax = b , x ? 0

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Efficiency of the simplex method

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Average case analysis of randomized quicksort

Starting on page 156 of Cormen, et al. Average case analysis of randomized quicksort. Running time is dominated by partition. The running time is dominated by the running time in partition procedure Each time partition is called, a pivot element is picked

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Time Analysis

Time Analysis. Since the time it takes to execute an algorithm usually depends on the size of the input, we express the algorithm's time complexity as a function of the size of the input

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