In this video, we look at the algorithmic complexity or asymptotic notation, we learn to measure it for any algorithm, and look at some of the common algorithmic complexities. After watching this video, you will be able to answer the following questions:
- What is an Algorithm?
- What is Algorithmic Complexity?
- What are Time Complexity and Space Complexity?
- How to measure Time Complexity?
- How to measure Space Complexity?
- What are Big O, Big Omega, and Big Theta notations?
- Why are we most interested in Big O notation over the others?
- What is Linear complexity?
- What is Quadratic complexity?
- What is Constant Complexity?
- What is Exponential Complexity?
- What is Logarithmic Complexity?
- What is the complexity that you should aim for?
0:00 Introduction
0:13 What is an Algorithm?
0:33 What is Algorithmic, Time, and Space Complexity?
0:42 Big O, Big Omega, Big Theta
1:09 Calculating Algorithmic Complexity
3:06 Common Complexities
3:17 Linear Complexity
3:59 Quadratic Complexity
4:42 Constant Complexity
5:08 Exponential Complexity
5:44 Logarithmic Complexity
6:51 Summary
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