Many students want to understand Time Complexity of algorithms but they don’t. Actually some of them don’t know what Time Complexity is.

First you have to understand a thing before writing a code we have to check how much time it’s algorithm would take to execute but that execution time should be platform independent (such as: operating system, programming language etc.)

Why would I am saying platform independent let’s understand with simple example:

We have a function of code written in java language, c language and php language. All of these languages will have different lines of code for each language mentioned. That’s why all codes in different languages will have different execution time. Thus, I had mentioned platform independent execution time for algorithms.

THIS EXECUTION TIME OF ALGORITHMS IS SO CALLED TIME COMPLEXITY.

There are various methods we can find Time complexity but the famous one is Big O notation like:
O(1), O(n), O(n2), O(log n) etc.

Let’s take an example to understand what these notations are:

If you have a list of 100 members in a company and you want to get your stolen wallet from them. Here are the ways to find the wallet.

Big O(n) Time Complexity
This notation tells that you go individually to each employee and ask for your wallet.

Big O(n2) Time Complexity
This notation tells that you go to each person and ask him to go and ask everyone for his wallet.

Big O(log n) Time Complexity
This notation tells that you divide the members equally in a left and right groups and ask is your wallet is on left side or right side and then you take that group divide it in two groups and ask again. Repeat the process till you find the member having your wallet.

Example using Java language Code:

1.     This code having O(1) Time Complexity.

public static void main(String arg[]) {
System.out.println(“hello world”);
}

2.     This Code having O(n2) Time Complexity.

public static void main(String arg[]) {
for (int i = 1; i <= n; i++) {
System.out.println(“hello world”);
}
}

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