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Analysis of Algorithms
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Analysis of Algorithms 2.1. Efficiency of Algorithms 2.2. Apriori Analysis 2.3. Asymptotic Notations 2.4. Time Complexity of an Algorithm using O (big-O) notation

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  • January 11, 2021
  • 45
  • 2020/2021
  • Lecture notes
  • Mr. mohd alif hasmani bin abd ghani
  • All classes

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Algorithm Analysis
with

Big Oh

Data Structures and Design with Java and JUnit
Chapter 12
©Rick Mercer

, Algorithm Analysis
w Objectives
⎯ Analyze the efficiency of algorithms
⎯ Analyze a few classic algorithms
• Linear Search, Binary Search, Selection Sort
⎯ Know the differences between O(1), O(n),
O(log n), and O(n2)
⎯ Visualize runtime differences with
experiments

, Algorithms continued

w Computer Scientists focus on problems such as
⎯ How fast do algorithms run
⎯ How much memory does the process require
w Example Applications
⎯ Make the Internet run faster
• Pink-Degemark's routing algorithms
• Gene Meyers determined the sequences of the Human
genome using his whole genome shotgun algorithm

, Analysis of Algorithms

w We have ways to compare algorithms
⎯ Generally, the larger the problem, the longer it
takes the algorithm to complete
⎯ Sorting 100,000 elements can take much more
time than sorting 1,000 elements
• and more than 10 times longer
⎯ the variable n suggests the "number of things"
⎯ If an algorithm requires 0.025n2 + 0.012n +
0.0005 seconds, just plug in a value for n

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