This lesson we talk about Anything That’s Not Measured Cannot Be Imporved. Big O. I hope this lesson is useful for you.

First Who is Big O?

In computer Science, Big O notation describes how the run time or space requirements of a program increases as the lenght of input increases.

Note: This is just the definition. Explanation coming up. Read this again after finishing the whole thing.

Big O alone can be used to describe how good an algorithm [or program] is upto a level

Big-O Complexity Chart, you can get form this site

O(1)O(log n)O(n)O(n log n)O(n^2)O(2^n)O(n^n)
Big-O Time Complexity

Big O can be used to explan how long a program will take to execute and how much memory it will consume regardless of the hardware used

This is the very reason that makes Big O special

Not considering the hardware and insterd considering the algorithm [steps taken to do a task]

+Time Complexity

+Space Complexity

These are just cool terms used to signify Big O for time a big O for space

An algorithm [program here] is supposed to take linear time if its time complexity is O[N]

  • An algorithm is supposed to take constant time if its time comlexity is O[1]

How go back and take a closer look at the animation we saw earlier

Question: What will be the time comlexity of an algorithm which takes a quadratic time.