'Lower order terms' diaporamas de présentation

Lecture 9: Recurrences and AVL Trees

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Complexity Analysis : Asymptotic Analysis

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CMSC 341

Asymptotic Analysis. CMSC 341. Complexity. How many resources will it take to solve a problem of a given size? time space Expressed as a function of problem size (beyond some minimum size) how do requirements grow as size grows? Problem size number of elements to be handled

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Algorithm Analysis

Algorithm Analysis. Introduction. Data structures Methods of organizing data What is Algorithm? a clearly specified set of simple instructions on the data to be followed to solve a problem Takes a set of values, as input and produces a value, or set of values, as output

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Algorithm Analysis

Algorithm Analysis. Introduction. Data structures Methods of organizing data What is Algorithm? a clearly specified set of simple instructions on the data to be followed to solve a problem Takes a set of values, as input and produces a value, or set of values, as output

616 views • 47 slides

CMSC 341

CMSC 341. Asymptotic Analysis. Complexity. How many resources will it take to solve a problem of a given size? time space Expressed as a function of problem size (beyond some minimum size) how do requirements grow as size grows? Problem size number of elements to be handled

591 views • 47 slides

CMSC 341

Asymptotic Analysis. CMSC 341. Complexity. How many resources will it take to solve a problem of a given size? time space Expressed as a function of problem size (beyond some minimum size) how do requirements grow as size grows? Problem size number of elements to be handled

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CS 311 Design and Algorithms Analysis

CS 311 Design and Algorithms Analysis. Dr. Mohamed Tounsi mtounsi@cis.psu.edu.sa. Problems and Algorithms. Abstractly, a problem is just a function p : Problem instances -> Solutions Example: Sorting a list of integers Problem instances = lists of integers

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CMSC 341

CMSC 341. Asymptotic Analysis. Complexity. How many resources will it take to solve a problem of a given size? time space Expressed as a function of problem size (beyond some minimum size) how do requirements grow as size grows? Problem size number of elements to be handled

550 views • 39 slides

CMSC 341

CMSC 341. Asymptotic Analysis. Complexity. How many resources will it take to solve a problem of a given size? time space Expressed as a function of problem size (beyond some minimum size) how do requirements grow as size grows? Problem size number of elements to be handled

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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

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MPI Program Performance

MPI Program Performance . Introduction. Defining the performance of a parallel program is more complex than simply optimizing its execution time. This is because of the large number of variables that can influence a program's behavior. Some of these variables are the number of processors

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MA557/MA578/CS557 Lecture 30

MA557/MA578/CS557 Lecture 30. Spring 2003 Prof. Tim Warburton timwar@math.unm.edu. Special Edition of ANUM on Absorbing Boundary Conditions. Structure of PML Regions. PEC. How Thick Does the PML Region Need To Be. Suppose we consider the region in blue [a,a+delta] And we set:. PEC.

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Analysis Tools

Analysis Tools. Jyh-Shing Roger Jang ( ??? ) CSIE Dept, National Taiwan University. Several Basic Functions. Polynomial Constant function Linear function Quadratic function Cubic function Logarithm function N-Log-N function Exponential function. Commonly used basic functions.

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Analysis of Algorithms

Analysis of Algorithms. Input. Algorithm. Output. Running Time. Most algorithms transform input objects into output objects. The running time of an algorithm typically grows with the input size. Average case time is often difficult to determine. We focus on the worst case running time.

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Chapter 4 Analysis Tools

Chapter 4 Analysis Tools. Objectives Experiment analysis of algorithms and limitations Theoretical Analysis of algorithms Pseudo-code description of algorithms Big-Oh notations Seven functions Proof techniques. Analysis of Algorithms. Input. Algorithm. Output.

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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.

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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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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.

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The Growth of Functions

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