1/9/22, 10:55 AM
Learning Guide Unit 2
Overview
Unit 2: Algorithm Analysis
Topics:
: Best, worst, and average cases for algorithm performance
: Asymptotic analysis
o Upper and Lower Bounds
o Big O, Big Omega, and Big Theta notation
: Determining the running time of an algorithm
Learning Objectives:
: Understand and be able to apply basic concepts of Asymptotic analysis
Recognize the cost /benefit tradeoffs that are inherent in the design of algorithms and the role of Asymptotic analysis to understand those
characteristics in specific algorithms and data structures
: Be able to determine the best, worst, and average case performance of a particular algorithm and be able to identify and articulate for any algorithm
o The upper bound in Big O (O) notation for the algorithm
o The lower bound in Big Omega () notation for the algorithm
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1/9/22, 10:55 AM
Learning Guide Unit 2
o The notation used when the upper and lower bounds are the same which is the Big Theta (O) notation for the algorithm
: Recognize and be able to apply the simplifying rules outlined in section 3.4.4 of the Shaffer text.
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