Asymptotic Analysis and Algorithm Performance, Lecture notes of Data Structures and Algorithms

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Algorithm performance

Asymptotic analysis

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License by Christine Alvarado, Mia Minnes, and Leo Porter, 2015.

Drop constants 10000000 = O(1)

Keep only dominant term fastest growing

Keep only dominant term 3n+3 = O(3n)

Examples?

 f(n) = 3 log_2 n + 4n log_2 n + n. Which of the following is true?  f(n) = O(log_2 n)  f(n) = O(n log_2 n)  f(n) = O(n^2) ** In response, industry / academic use of O  f(n) = O(n)  f(n) = 100. Which of the following is true?  f(n) = O(2^n)  f(n) = O(n^2)  f(n) = O(n)  f(n) = O(1) IVQ

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